A Study of the system control model of caisson dewatering of the north anchorage of Taizhou Brid
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- disruption ，: Global convergence vs nationalSustainable - ，practices and dynamic capabilities in the food industry: A critical analysis of the literature5 Mesoscopic - simulation6 Firm size and sustainable performance in food -s: Insights from Greek SMEs7 An analytical method for cost analysis in multi-stage -s: A stochastic / model approach8 A Roadmap to Green - System through Enterprise Resource Planning (ERP) Implementation9 Unidirectional transshipment policies in a dual-channel -10 Decentralized and centralized model predictive control to reduce the bullwhip effect in - ，11 An agent-based distributed computational experiment framework for virtual - / development12 Biomass-to-bioenergy and biofuel - optimization: Overview, key issues and challenges13 The benefits of - visibility: A value assessment model14 An Institutional Theory perspective on sustainable practices across the dairy -15 Two-stage stochastic programming - model for biodiesel production via wastewater treatment16 Technology scale and -s in a secure, affordable and low carbon energy transition17 Multi-period design and planning of closed-loop -s with uncertain supply and demand18 Quality control in food - ，: An analytical model and case study of the adulterated milk incident in China19 - information capabilities and performance outcomes: An empirical study of Korean steel suppliers20 A game-based approach towards facilitating decision making for perishable products: An example of blood -21 - design under quality disruptions and tainted materials delivery22 A two-level replenishment frequency model for TOC - replenishment systems under capacity constraint23 - dynamics and the ―cross-border effect‖: The U.S.–Mexican border’s case24 Designing a new - for competition against an existing -25 Universal supplier selection via multi-dimensional auction mechanisms for two-way competition in oligopoly market of -26 Using TODIM to evaluate green - practices under uncertainty27 - downsizing under bankruptcy: A robust optimization approach28 Coordination mechanism for a deteriorating item in a two-level - system29 An accelerated Benders decomposition algorithm for sustainable - / design under uncertainty: A case study of medical needle and syringe -30 Bullwhip Effect Study in a Constrained -31 Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable - / of perishable food32 Research on pricing and coordination strategy of green - under hybrid production mode33 Agent-system co-development in - research: Propositions and demonstrative findings34 Tactical ，for coordinated -s35 Photovoltaic - coordination with strategic consumers in China36 Coordinating supplier׳s reorder point: A coordination mechanism for -s with long supplier lead time37 Assessment and optimization of forest biomass -s from economic, social and environmental perspectives – A review of literature38 The effects of a trust mechanism on a dynamic - /39 Economic and environmental assessment of reusable plastic containers: A food catering - case study40 Competitive pricing and ordering decisions in a multiple-channel -41 Pricing in a - for auction bidding under information asymmetry42 Dynamic analysis of feasibility in ethanol - for biofuel production in Mexico43 The impact of partial information sharing in a two-echelon -44 Choice of - governance: Self-managing or outsourcing?45 Joint production and delivery lot sizing for a make-to-order producer–buyer - with transportation cost46 Hybrid algorithm for a vendor managed inventory system in a two-echelon -47 Traceability in a food -: Safety and quality perspectives48 Transferring and sharing exchange-rate risk in a risk-averse - of a multinational firm49 Analyzing the impacts of carbon regulatory mechanisms on supplier and mode selection decisions: An application to a biofuel -50 Product quality and return policy in a - under risk aversion of a supplier51 Mining logistics data to assure the quality in a sustainable food -: A case in the red wine industry52 Biomass - optimisation for Organosolv-based biorefineries53 Exact solutions to the - equations for arbitrary, time-dependent demands54 Designing a sustainable closed-loop - / based on triple bottom line approach: A comparison of metaheuristics hybridization techniques55 A study of the LCA based biofuel - multi-objective optimization model with multi-conversion paths in China56 A hybrid two-stock inventory control model for a reverse -57 Dynamics of judicial service -s58 Optimizing an integrated vendor-managed inventory system for a single-vendor two-buyer - with determining weighting factor for vendor׳s ordering59 Measuring - Resilience Using a Deterministic Modeling Approach60 A LCA Based Biofuel - Analysis Framework61 A neo-institutional perspective of -s and energy security: Bioenergy in the UK62 Modified penalty function method for optimal social welfare of electric power - with transmission constraints63 Optimization of blood - with shortened shelf lives and ABO compatibility64 Diversified firms on dynamical - cope with financial crisis better65 Securitization of energy -s in China66 Optimal design of the auto parts - for JIT operations: Sequential bifurcation factor screening and multi-response surface methodology67 Achieving sustainable -s through energy justice68 - agility: Securing performance for Chinese manufacturers69 Energy price risk and the sustainability of demand side -s70 Strategic and tactical mathematical programming models within the crude oil - context - A review71 An analysis of the structural complexity of - /s72 Business process re-design methodology to support - integration73 Could - technology improve food operators’ innovativeness? A developing country’s perspective74 RFID-enabled process reengineering of closed-loop -s in the healthcare industry of Singapore75 Order-Up-To policies in Information Exchange -s76 Robust design and operations of hydrocarbon biofuel - integrating with existing petroleum refineries considering unit cost objective77 Trade-offs in - transparency: the case of Nudie Jeans78 Healthcare - operations: Why are doctors reluctant to consolidate?79 Impact on the optimal design of bioethanol -s by a new European Commission proposal80 Managerial research on the pharmaceutical - – A critical review and some insights for future directions81 - performance evaluation with data envelopment analysis and balanced scorecard approach82 Integrated - design for commodity chemicals production via woody biomass fast pyrolysis and upgrading83 Governance of sustainable -s in the fast fashion industry84 Temperature ，for the quality assurance of a perishable food -85 Modeling of biomass-to-energy - operations: Applications, challenges and research directions86 Assessing Risk Factors in Collaborative - with the Analytic Hierarchy Process (AHP)87 Random / models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage -s88 Information sharing and collaborative behaviors in enabling - performance: A social exchange perspective89 The coordinating contracts for a fuzzy - with effort and price dependent demand90 Criticality analysis and the -: Leveraging representational assurance91 Economic model predictive control for inventory ，in -s92 - ，ontology from an ontology engineering perspective93 Surplus division and investment incentives in -s: A biform-game analysis94 Biofuels for road transport: Analysing evolving -s in Sweden from an energy security perspective95 - ，executives in corporate upper echelons Original Research Article96 Sustainable - ，in the fast fashion industry: An analysis of corporate reports97 An improved method for managing catastrophic - disruptions98 The equilibrium of closed-loop - super/ with time-dependent parameters99 A bi-objective stochastic programming model for a centralized green - with deteriorating products100 Simultaneous control of vehicle routing and inventory for dynamic inbound -101 Environmental impacts of roundwood - options in Michigan: life-cycle assessment of harvest and transport stages102 A recovery mechanism for a two echelon - system under supply disruption103 Challenges and Competitiveness Indicators for the Sustainable Development of the - in Food Industry104 Is doing more doing better? The relationship between responsible - ，and corporate reputation105 Connecting product design, process and - decisions to strengthen global - capabilities106 A computational study for common / design in multi-commodity -s107 Optimal production and procurement decisions in a - with an option contract and partial backordering under uncertainties108 Methods to optimise the design and ，of biomass-for-bioenergy -s: A review109 Reverse - coordination by revenue sharing contract: A case for the personal computers industry110 SCOlog: A logic-based approach to analysing - operation dynamics111 Removing the blinders: A literature review on the potential of nanoscale technologies for the ，of -s112 Transition inertia due to competition in -s with remanufacturing and recycling: A systems dynamics mode113 Optimal design of advanced drop-in hydrocarbon biofuel - integrating with existing petroleum refineries under uncertainty114 Revenue-sharing contracts across an extended -115 An integrated revenue sharing and quantity discounts contract for coordinating a - dealing with short life-cycle products116 Total JIT (T-JIT) and its impact on - competency and organizational performance117 Logistical - design for bioeconomy applications118 A note on ―Quality investment and inspection policy in a supplier-manufacturer -‖119 Developing a Resilient -120 Cyber - risk ，: Revolutionizing the strategic control of critical IT systems121 Defining value chain architectures: Linking strategic value creation to operational - design122 Aligning the sustainable - to green marketing needs: A case study123 Decision support and intelligent systems in the textile and apparel -: An academic review of research articles124 - ，capability of small and medium sized family businesses in India: A multiple case study approach125 - collaboration: Impact of success in long-term partnerships126 Collaboration capacity for sustainable - ，: small and medium-sized enterprises in Mexico127 Advanced traceability system in aquaculture -128 - information systems strategy: Impacts on - performance and firm performance129 Performance of - collaboration – A simulation study130 Coordinating a three-level - with delay in payments and a discounted interest rate131 An integrated framework for agent basedinventory–production–transportation modeling and distributed simulation of -s132 Optimal - design and ，over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models133 The impact of knowledge transfer and complexity on - flexibility: A knowledge-based view134 An innovative - performance measurement system incorporating Research and Development (R&D) and marketing policy135 Robust decision making for hybrid process - systems via model predictive control136 Combined pricing and - operations under price-dependent stochastic demand137 Balancing - competitiveness and robustness through ―virtual dual sourcing‖: Lessons from the Great East Japan Earthquake138 Solving a tri-objective - problem with modified NSGA-II algorithm 139 Sustaining long-term - partnerships using price-only contracts 140 On the impact of advertising initiatives in -s141 A typology of the situations of cooperation in -s142 A structured analysis of operations and - ，research in healthcare (1982–2011143 - practice and information quality: A - strategy study144 Manufacturer's pricing strategy in a two-level - with competing retailers and advertising cost dependent demand145 Closed-loop - / design under a fuzzy environment146 Timing and eco(nomic) efficiency of climate-friendly investments in -s147 Post-seismic - risk ，: A system dynamics disruption analysis approach for inventory and logistics planning148 The relationship between legitimacy, reputation, sustainability and branding for companies and their -s149 Linking - configuration to - perfrmance: A discrete event simulation model150 An integrated multi-objective model for allocating the limited sources in a multiple multi-stage lean -151 Price and leadtime competition, and coordination for make-to-order -s152 A model of resilient - / design: A two-stage programming with fuzzy shortest path153 Lead time variation control using reliable shipment equipment: An incentive scheme for - coordination154 Interpreting - dynamics: A quasi-chaos perspective155 A production-inventory model for a two-echelon - when demand is dependent on sales teams׳ initiatives156 Coordinating a dual-channel - with risk-averse under a two-way revenue sharing contract157 Energy supply planning and - optimization under uncertainty158 A hierarchical model of the impact of RFID practices on retail - performance159 An optimal solution to a three echelon - / with multi-product and multi-period160 A multi-echelon - model for municipal solid waste ，system 161 A multi-objective approach to - visibility and risk162 An integrated - model with errors in quality inspection and learning in production163 A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge ，adoption in - to overcome its barriers164 A relational study of - agility, competitiveness and business performance in the oil and gas industry165 Cyber - security practices DNA – Filling in the puzzle using a diverse set of disciplines166 A three layer - model with multiple suppliers, manufacturers and retailers for multiple items167 Innovations in low input and organic dairy -s—What is acceptable in Europe168 Risk Variables in Wind Power -169 An analysis of - strategies in the regenerative medicine industry—Implications for future development170 A note on - coordination for joint determination of order quantity and reorder point using a credit option171 Implementation of a responsive - strategy in global complexity: The case of manufacturing firms172 - scheduling at the manufacturer to minimize inventory holding and delivery costs173 GBOM-oriented ，of production disruption risk and optimization of - construction175 Alliance or no alliance—Bargaining power in competing reverse -s174 Climate change risks and adaptation options across Australian seafood -s – A preliminary assessment176 Designing contracts for a closed-loop - under information asymmetry 177 Chemical - modeling for analysis of homeland security178 Chain liability in multitier -s? Responsibility attributions for unsustainable supplier behavior179 Quantifying the efficiency of price-only contracts in push -s over demand distributions of known supports180 Closed-loop - / design: A financial approach181 An integrated - / design problem for bidirectional flows182 Integrating multimodal transport into cellulosic biofuel - design under feedstock seasonality with a case study based on California183 - dynamic configuration as a result of new product development184 A genetic algorithm for optimizing defective goods - costs using JIT logistics and each-cycle lengths185 A - / design model for biomass co-firing in coal-fired power plants 186 Finance sourcing in a -187 Data quality for data science, predictive analytics, and big data in - ，: An introduction to the problem and suggestions for research and applications188 Consumer returns in a decentralized -189 Cost-based pricing model with value-added tax and corporate income tax for a - /190 A hard nut to crack! Implementing - sustainability in an emerging economy191 Optimal location of spelling yards for the northern Australian beef -192 Coordination of a socially responsible - using revenue sharing contract193 Multi-criteria decision making based on trust and reputation in -194 Hydrogen - architecture for bottom-up energy systems models. Part 1: Developing pathways195 Financialization across the Pacific: Manufacturing cost ratios, -s and power196 Integrating deterioration and lifetime constraints in production and - planning: A survey197 Joint economic lot sizing problem for a three—Layer - with stochastic demand198 Mean-risk analysis of radio frequency identification technology in - with inventory misplacement: Risk-sharing and coordination199 Dynamic impact on global -s performance of disruptions propagation produced by terrorist acts。
设计和开发控制程序、英语
设计和开发控制程序、英语Designing and Developing Control Programs in EnglishThe field of control systems has become increasingly important in modern technology, with applications spanning a wide range of industries, from manufacturing to transportation, and beyond. As the complexity of these systems continues to grow, the need for efficient and effective control programs has become paramount. In this essay, we will explore the process of designing and developing control programs, with a focus on the use of English as the primary language of communication and implementation.Firstly, it is essential to understand the fundamental principles of control systems. A control system is a collection of components that work together to maintain a desired state or output, often in response to external inputs or disturbances. These systems can be classified into two main categories: open-loop and closed-loop. Open-loop systems operate without feedback, while closed-loop systems use feedback to adjust the control actions and maintain the desired output.The design of a control program begins with a thoroughunderstanding of the system's requirements and constraints. This involves gathering information about the system's inputs, outputs, and the desired behavior. Engineers must also consider factors such as the system's physical limitations, environmental conditions, and any safety or regulatory requirements. Once the system requirements are clearly defined, the next step is to develop a mathematical model of the system's dynamics, which can be used to analyze its behavior and design the appropriate control strategies.One of the key aspects of control program design is the selection of the appropriate control algorithm. There are various control algorithms available, each with its own strengths and weaknesses, and the choice will depend on the specific requirements of the system. Some common control algorithms include proportional-integral-derivative (PID) control, model predictive control (MPC), and fuzzy logic control. Each of these algorithms has its own set of tuning parameters and implementation considerations, and engineers must carefully evaluate the trade-offs to select the most appropriate solution.The implementation of the control program is a critical step in the overall design process. This involves translating the mathematical model and control algorithm into a working software program that can be deployed on the target hardware. This process requires a strong understanding of programming languages, such as C, C++, orPython, as well as the specific hardware and software platforms used in the control system.One of the key challenges in implementing control programs is ensuring that the software is robust, reliable, and efficient. This requires careful attention to code structure, error handling, and performance optimization. Additionally, engineers must consider the integration of the control program with other system components, such as sensors, actuators, and user interfaces, to ensure seamless operation.Throughout the design and development process, clear and effective communication in English is essential. Engineers must be able to document their work, collaborate with team members, and communicate with stakeholders, such as customers, regulatory bodies, and other technical professionals. This requires a strong command of technical English, including the ability to use appropriate terminology, express complex technical concepts, and write clear and concise documentation.In conclusion, the design and development of control programs is a complex and multifaceted process that requires a deep understanding of control system principles, programming skills, and effective communication in English. By mastering these skills, engineers can create innovative and efficient control solutions thatdrive technological progress and improve the lives of people around the world.。
A_Study_on_the_Load_Modeling_of_Railway_Vehicles_U
Journal of Energy and Power Engineering 17 (2023) 43-50doi: 10.17265/1934-8975/2023.02.002A Study on the Load Modeling of Railway Vehicles Using PSCAD/EMTDC Based on MVDCHyun-Soo Jeong1, Hanmin Lee2 and Jong-Young Park21. Hyubwoojiyeu Engineering Co., Ltd., 243, Digital-ro, Guro-gu, Seoul, 08382, Republic of Korea2. Korea Railroad Research Institute, 176, Cheoldobangmulgwan-ro, Uiwang-si, Gyeonggi-do, 16105, Republic of KoreaAbstract: This paper, the kinetic equation, traction force, and braking force for railway trains are reviewed. In addition, the driving characteristics are interpreted as to how the power of the electric vehicle relates to the weight, speed, track curve, and track gradient of the electric vehicle. The driving characteristics of these trains are analyzed through PSCAD/EMTDC (power systems computer aided design/electromagnetic transients including DC) modeling.Key words: MVDC (medium voltage direct current), railway, load modeling, PSCAD/EMTDC.1. IntroductionDC-related technologies, such as HVDC (high-voltage direct current) and LVDC (low-voltage direct current), continue to be developed to increase connection capacity and improve efficiency of new and renewable energy. In the future, it is expected to introduce a medium-sized MVDC (medium voltage direct current) distribution network that can link HVDC and LVDC. Currently, there is no related market in the railway part. However, with the emergence of new MVDC-related equipment, it is expected that the relevant market will soon be formed. The electric railway system is one of the end users who consume a lot of power in KEPCO’s power grid. The electric railway system is greatly influenced by the development of MVDC grid technology. As a result, efforts to incorporate applied technologies in the railway sector are expected to lead to an increase in the size of the related market, so it is necessary to model trains for interpretation when applying the MVDC distribution network.Accurate modeling of the motion and power consumption of railway vehicles is needed. In order toCorresponding author: Hanmin Lee, Ph.D., chief researcher, research fields: power quality, propulsion control device and vehicle characteristics analysis. know the exact braking characteristics of the vehicle, based on the relationship between wheel rotation, train traction, and braking [1], Jeon et al. [2] and Kim et al.[3] estimated the traction and braking power for electric locomotives and Korean high-speed trains and compared them with the test results. Choi et al. [4] derived and tested acceleration changes for HEMU-430X, a high-speed train that is a power-distributed train, and Kim et al. [5] presented maximum acceleration values and specifications when implementing high-performance cars for next-generation trains. Therefore, in this paper, the kinetic equation, traction force, and braking force for railway trains are reviewed. In addition, the driving characteristics are interpreted as to how the power of the electric vehicle relates to the weight, speed, track curve, and track gradient of the electric vehicle. The driving characteristics of these trains are analyzed through PSCAD/EMTDC modeling.2. Train Operation Relationship FormulaThe electric railway system is a vast system that includes a number of train groups and operations, tracks, and electrical installations. In order to perform theD A VID PUBLISHINGDA Study on the Load Modeling of Railway Vehicles Using PSCAD/EMTDC Based on MVDC44Fig. 1 Standard operation curve.simulation, numerous input data and conditions mustbe set. Most railway vehicles maintain a simple patternof starting-accelerating-coasting-braking-stop and operateon the basis of the standard driving curve shown in Fig. 1.The electric vehicle generates reverse and regenerativecurrents according to speed and position. The railwayvehicle obtains acceleration and deceleration to determinethe following driving conditions and speeds. Therefore,the railway vehicle has one of the operation modes shownin Fig. 1. That is, the next position is determined accordingto the speed of the electric vehicle. When the speed andposition are determined, the operation mode is determinedaccording to the standard operation curve [6-8].The basic formulas for train operation are as shownin Eqs. (1) and (2).v=dsdt(1)where,v: speed (km/h) s: distance (m) t: time (s)a=dvdt(2)where, a: acceleration(km/h/s).Eqs. (1) and (2) represent relational equations for position, speed, and acceleration in linear motion. When the acceleration is constant, the function v(t) of the velocity with respect to time and the function x(t) of the distance are as shown in Eqs. (3) and (4).v(t)=v0+at(3)v(t)=12at2+v0t+S0(4) The acceleration a train can produce is related to the traction force of the motor and the resistance of the train. The acceleration of the train is the value obtained by dividing the effective traction by the dynamic mass, as shown in Eq. (5).a=F effm dyn(5) where,F eff: effective traction (kN)M dyn: dynamic mass (ton)The dynamic mass includes the dynamic mass in the full vehicle mass, which takes into account the force required by rotating the wheels of the train as well as linear motion.The dynamic mass is obtained by multiplying the M-car by the compensation coefficient of 0.14 and the T difference by the compensation coefficient of 0.06.M dyn = 0.14×M m + 0.06×M t(6) where,M dyn: dynamic mass (ton)M m: M-Car overall tolerance weight (ton)M t: T-Car overall tolerance weight (ton)0.14: M-Car inertial mass compensation factor0.06: T-Car inertial mass compensation factorThe effective traction force is the value obtained bysubtracting the train resistance from the motor tractionA Study on the Load Modeling of Railway Vehicles Using PSCAD/EMTDC Based on MVDC 45force, as shown in Eq. (7).F eff =F mtf −R (7)where,F eff : effective traction (kN) F mtf : electric traction (kN) R : train resistance (kN)In this case, the train resistance R is the sum of the curve resistance, the running resistance, and the gradient resistance, and is expressed as Eq. (7), and the resistance is as shown in Eq. (8).R =R curve + R run + R gradient (8)where,R curve : curve resistance (kN) R run : running resistance (kN) R gradient : gradient resistance (kN) Curve resistanceR curve =700× W fullr×9.8×10−3 (kN)(9)where,R curve : curve resistance (kN) W full : full load weight (ton) r : curved radius (m) Running resistance-R run = 1.867 + 0.0359V + 0.000745V 2 (kgf/ton) (10) where,V : vehicle speed (km/h) Gradient resistanceR gradient =G ×W full ×9.8×10−3 (kN) (11) where,R gradient : gradient resistance (kN) W full : full load weight (ton) G : gradient (‰)3. Traction and Braking ForcesThe traction force and braking force are calculated by applying the following formula.F (N) = m (kg) × a (m/s 2) + r (N)(12)In the above equation, the units are converted into(kN) and (ton) as follows.F (kN) = M (ton) × a (m/s 2) + R (kN)(13)where,M: Train full weight including inertial mass (ton) (M = W full + M dyn )W full : load weight (ton) M dyn : dynamic mass (ton) R : train resistance (kN)The railway vehicle travels at the same acceleration up to 35 km/h as shown in Fig. 2, subsequently the acceleration decreases and the speed increases at full speed.The measurement data of the next-generation electric vehicle developed by the Korea Railroad Research Institute were compared with the simulation. Fig. 3 is the measurement data related to traction over time, and it was found that the train performs uniformally accelerated motion up to 35 km/h. It can be seen that the speed increases to the maximum speed as the acceleration decreases after 35 km/h.Fig. 2Speed curve over time.A Study on the Load Modeling of Railway Vehicles Using PSCAD/EMTDC Based on MVDC46Fig. 3 Measurement data related to traction over time.Fig. 4 Simulation data related to traction over time.Fig. 5Measurement data related to braking force over time.A Study on the Load Modeling of Railway Vehicles Using PSCAD/EMTDC Based on MVDC 47Fig. 6 Simulation data related to braking force over time.Fig. 4 is the result of the simulation of traction over time, and as a result of comparing the simulation data related to traction with the measurement data, the corresponding time for each speed was the same.Fig. 5 shows the measurement data for braking force over time, and the electric braking power reached the maximum in 14 seconds. Fig. 6 is the result of simulating the braking force over time. In the same way as the measurement data, the braking force was maximized at 14 seconds. Therefore, as a result of comparing the braking force-related simulation data with the measurement data, the corresponding time for each speed was the same.4. Modeling and Analysis of Constant Power Vehicles Based on Driving CharacteristicsThe vehicle model has been modeled based on the driving characteristics described earlier. The vehiclehas been implemented using PSCAD/EMTDC as a current source reflecting the vehicle’s driving characteristics as shown in Fig. 7.Various vehicle modes of operation can be implemented. The speed curves according to traction, coasting and braking operation modes are shown in Fig. 8. The traction and braking forces are shown in Figs. 9 and 10, respectively.Fig. 7 Constant power load modeling.Fig. 8Operation Mode accelerating-costing-breaking.A Study on the Load Modeling of Railway Vehicles Using PSCAD/EMTDC Based on MVDC48Fig. 9 Traction force according to operation mode.Fig. 10 Braking force according to operation mode.Fig. 11 Traction power and regenerative power according to the operation mode.A Study on the Load Modeling of Railway Vehicles Using PSCAD/EMTDC Based on MVDC 49 Fig. 12 Catenary voltage.Fig. 11 shows the traction power and regenerative power according to the operation mode of Fig. 8, and Fig. 12 shows the catenary voltage, and it can be seen that the voltage drops below 1.5 kV when the vehicle is towed and rises to 1.5 kV or more when regenerating.5. ConclusionIn this paper, the equations of motion, traction and braking of the vehicle were reviewed. In addition, the driving characteristics are interpreted as to how the power of the electric vehicle relates to the weight, speed, track curve, and track gradient of the electric vehicle. The driving characteristics of these trains are analyzed through PSCAD/EMTDC modeling.Simulation results and measurement data for traction and braking over time were compared. As a result, simulation data and measurement data showed the same time for each speed. The results showed that the vehicle model was properly implemented with PSCAD/EMTDC.AcknowledgmentsThis work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20225500000110).References[1]Lee, S. K. 2010. “A Study on Optimal Design of TractionMotor Power for Urban Transit.” M.Sc. Thesis, HanyangUniversity.[2]Jeon, H. J., Kim, C. H., and Lim, J. H. 2007. “Test andTraction Characteristic of Electric Locomotives.” InProceedings of the Conference of the Korean Society forRailway, Jeju, pp. 40-7.[3]Kim, Y., Kim, S., Kim, K., and Mok, J. 2006. “Study onthe Deduction of Traction/Braking Forces for the Trainfrom Acceleration/Deceleration.” Journal of the KoreanSociety for Railway 9 (6): 682-8.[4]Choi, D., Jeon, C. S., Cho, H., Oh, H. K., and Kim, S. 2013.“The Relationship between Train Weight and Accelerationfor the Korea’s Next Generation Electric Multiple UnitTrain.” In Proceedings of the Conference of the KoreanSociety for Railway, Daegu, pp. 470-4.[5]Kim, J., Kim, M. S., Ko, K., and Jang, D. U. 2015. “TheStudy on the Standardization of the MaximumAcceleration of the Electric Multiple Unit through theAnalysis of the Traction and the Adhesion Characteristics.”Journal of the Korea Academia-Industrial CooperationSociety 16 (11): 7934-40.[6]Energy In. Co., Ltd. 2020. Energy CharacteristicsofA Study on the Load Modeling of Railway Vehicles Using PSCAD/EMTDC Based on MVDC50Electric Vehicles when Applying On-Board ESS of Battery Packs for Railway Vehicles.[7]Woojin Industrial System Co. 2006. A Study on theApplication of Energy Storage Technology System toRailway Stations. Korea: Woojin Industrial System Co.[8]Korea University. 2002. “Development and Application ofUrban Railway Exchange Supply System Using PSCAD/EMTDC.”。
脓毒症心肌功能障碍模型大鼠的构建与评价
《中国组织工程研究》 Chinese Journal of Tissue Engineering Research1249·研究原著·张胜，男，1989年生，河北省沧州市人，回族，宁夏医科大学在读硕士，医师，主要从事重症医学研究。
通讯作者：王晓红，博士，主任医师，副教授，硕士生导师，宁夏医科大学总医院ICU 科，宁夏回族自治区银川市 750004文献标识码:B投稿日期：2019-07-03 送审日期：2019-07-05 采用日期：2019-08-21 在线日期：2019-10-23Zhang Sheng, Master candidate, Physician, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, ChinaCorresponding author: Wang Xiaohong, MD, Chief physician, Associate professor, ICU, General Hospital of Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China脓毒症心肌功能障碍模型大鼠的构建与评价张 胜1，白吉佳2，徐艳萍3，王晓红2 (1宁夏医科大学，宁夏回族自治区银川市 750004；宁夏医科大学总医院，2ICU 科，3心脏中心功能检查部，宁夏回族自治区银川市 750004)DOI:10.3969/j.issn.2095-4344.2464 ORCID: 0000-0002-9840-8260(张胜)文章快速阅读：文题释义：脓毒症心肌功能障碍：是脓毒症或脓毒症休克最严重的并发症之一，其特征为心脏整体可逆的功能障碍，但心功能障碍的严重程度仍是影响脓毒症预后的重要因素之一，若不能及时有效地改善脓毒症心肌功能障碍将显著增加脓毒症患者病死率。
（完整版）自动控制专业英语词汇
（完整版）自动控制专业英语词汇自动控制专业英语词汇（一）acceleration transducer 加速度传感器acceptance testing 验收测试accessibility 可及性accumulated error 累积误差AC-DC-AC frequency converter 交-直-交变频器AC (alternating current) electric drive 交流电子传动active attitude stabilization 主动姿态稳定actuator 驱动器，执行机构adaline 线性适应元adaptation layer 适应层adaptive telemeter system 适应遥测系统adjoint operator 伴随算子admissible error 容许误差aggregation matrix 集结矩阵AHP (analytic hierarchy process) 层次分析法amplifying element 放大环节analog-digital conversion 模数转换annunciator 信号器antenna pointing control 天线指向控制anti-integral windup 抗积分饱卷aperiodic decomposition 非周期分解a posteriori estimate 后验估计approximate reasoning 近似推理a priori estimate 先验估计articulated robot 关节型机器人assignment problem 配置问题，分配问题associative memory model 联想记忆模型associatron 联想机asymptotic stability 渐进稳定性attained pose drift 实际位姿漂移attitude acquisition 姿态捕获AOCS (attritude and orbit control system) 姿态轨道控制系统attitude angular velocity 姿态角速度attitude disturbance 姿态扰动attitude maneuver 姿态机动attractor 吸引子augment ability 可扩充性augmented system 增广系统automatic manual station 自动-手动操作器automaton 自动机autonomous system 自治系统backlash characteristics 间隙特性base coordinate system 基座坐标系Bayes classifier 贝叶斯分类器bearing alignment 方位对准bellows pressure gauge 波纹管压力表benefit-cost analysis 收益成本分析bilinear system 双线性系统biocybernetics 生物控制论biological feedback system 生物反馈系统black box testing approach 黑箱测试法blind search 盲目搜索block diagonalization 块对角化Boltzman machine 玻耳兹曼机bottom-up development 自下而上开发boundary value analysis 边界值分析brainstorming method 头脑风暴法breadth-first search 广度优先搜索butterfly valve 蝶阀CAE (computer aided engineering) 计算机辅助工程CAM (computer aided manufacturing) 计算机辅助制造Camflex valve 偏心旋转阀canonical state variable 规范化状态变量capacitive displacement transducer 电容式位移传感器capsule pressure gauge 膜盒压力表CARD 计算机辅助研究开发Cartesian robot 直角坐标型机器人cascade compensation 串联补偿catastrophe theory 突变论centrality 集中性chained aggregation 链式集结chaos 混沌characteristic locus 特征轨迹chemical propulsion 化学推进calrity 清晰性classical information pattern 经典信息模式classifier 分类器clinical control system 临床控制系统closed loop pole 闭环极点closed loop transfer function 闭环传递函数cluster analysis 聚类分析coarse-fine control 粗-精控制cobweb model 蛛网模型coefficient matrix 系数矩阵cognitive science 认知科学cognitron 认知机coherent system 单调关联系统combination decision 组合决策combinatorial explosion 组合爆炸combined pressure and vacuum gauge 压力真空表command pose 指令位姿companion matrix 相伴矩阵compartmental model 房室模型compatibility 相容性，兼容性compensating network 补偿网络compensation 补偿，矫正compliance 柔顺，顺应composite control 组合控制computable general equilibrium model 可计算一般均衡模型conditionally instability 条件不稳定性configuration 组态connectionism 连接机制connectivity 连接性conservative system 守恒系统consistency 一致性constraint condition 约束条件consumption function 消费函数context-free grammar 上下文无关语法continuous discrete event hybrid system simulation 连续离散事件混合系统仿真continuous duty 连续工作制control accuracy 控制精度control cabinet 控制柜controllability index 可控指数controllable canonical form 可控规范型[control] plant 控制对象,被控对象controlling instrument 控制仪表control moment gyro 控制力矩陀螺control panel 控制屏，控制盘control synchro 控制[式]自整角机control system synthesis 控制系统综合control time horizon 控制时程cooperative game 合作对策coordinability condition 可协调条件coordination strategy 协调策略coordinator 协调器corner frequency 转折频率costate variable 共态变量cost-effectiveness analysis 费用效益分析coupling of orbit and attitude 轨道和姿态耦合critical damping 临界阻尼critical stability 临界稳定性cross-over frequency 穿越频率，交越频率current source inverter 电流[源]型逆变器cut-off frequency 截止频率cybernetics 控制论cyclic remote control 循环遥控cylindrical robot 圆柱坐标型机器人damped oscillation 阻尼振荡damper 阻尼器damping ratio 阻尼比data acquisition 数据采集data encryption 数据加密data preprocessing 数据预处理data processor 数据处理器DC generator-motor set drive 直流发电机-电动机组传动D controller 微分控制器decentrality 分散性decentralized stochastic control 分散随机控制decision space 决策空间decision support system 决策支持系统decomposition-aggregation approach 分解集结法decoupling parameter 解耦参数deductive-inductive hybrid modeling method 演绎与归纳混合建模法delayed telemetry 延时遥测derivation tree 导出树derivative feedback 微分反馈describing function 描述函数desired value 希望值despinner 消旋体destination 目的站detector 检出器deterministic automaton 确定性自动机deviation 偏差deviation alarm 偏差报警器DFD 数据流图diagnostic model 诊断模型diagonally dominant matrix 对角主导矩阵diaphragm pressure gauge 膜片压力表difference equation model 差分方程模型differential dynamical system 微分动力学系统differential game 微分对策differential pressure level meter 差压液位计differential pressure transmitter 差压变送器differential transformer displacement transducer 差动变压器式位移传感器differentiation element 微分环节digital filer 数字滤波器digital signal processing 数字信号处理digitization 数字化digitizer 数字化仪dimension transducer 尺度传感器direct coordination 直接协调disaggregation 解裂discoordination 失协调discrete event dynamic system 离散事件动态系统discrete system simulation language 离散系统仿真语言discriminant function 判别函数displacement vibration amplitude transducer 位移振幅传感器dissipative structure 耗散结构distributed parameter control system 分布参数控制系统distrubance 扰动disturbance compensation 扰动补偿diversity 多样性divisibility 可分性domain knowledge 领域知识dominant pole 主导极点dose-response model 剂量反应模型dual modulation telemetering system 双重调制遥测系统dual principle 对偶原理dual spin stabilization 双自旋稳定duty ratio 负载比dynamic braking 能耗制动dynamic characteristics 动态特性dynamic deviation 动态偏差dynamic error coefficient 动态误差系数dynamic exactness 动它吻合性dynamic input-output model 动态投入产出模型econometric model 计量经济模型economic cybernetics 经济控制论economic effectiveness 经济效益economic evaluation 经济评价economic index 经济指数economic indicator 经济指标eddy current thickness meter 电涡流厚度计effectiveness 有效性effectiveness theory 效益理论elasticity of demand 需求弹性electric actuator 电动执行机构electric conductance levelmeter 电导液位计electric drive control gear 电动传动控制设备electric hydraulic converter 电-液转换器electric pneumatic converter 电-气转换器electrohydraulic servo vale 电液伺服阀electromagnetic flow transducer 电磁流量传感器electronic batching scale 电子配料秤electronic belt conveyor scale 电子皮带秤electronic hopper scale 电子料斗秤elevation 仰角emergency stop 异常停止empirical distribution 经验分布endogenous variable 内生变量equilibrium growth 均衡增长equilibrium point 平衡点equivalence partitioning 等价类划分ergonomics 工效学error 误差error-correction parsing 纠错剖析estimate 估计量estimation theory 估计理论evaluation technique 评价技术event chain 事件链evolutionary system 进化系统exogenous variable 外生变量expected characteristics 希望特性external disturbance 外扰fact base 事实failure diagnosis 故障诊断fast mode 快变模态feasibility study 可行性研究feasible coordination 可行协调feasible region 可行域feature detection 特征检测feature extraction 特征抽取feedback compensation 反馈补偿feedforward path 前馈通路field bus 现场总线finite automaton 有限自动机FIP (factory information protocol) 工厂信息协议first order predicate logic 一阶谓词逻辑fixed sequence manipulator 固定顺序机械手fixed set point control 定值控制FMS (flexible manufacturing system) 柔性制造系统flow sensor/transducer 流量传感器flow transmitter 流量变送器fluctuation 涨落forced oscillation 强迫振荡formal language theory 形式语言理论formal neuron 形式神经元forward path 正向通路forward reasoning 正向推理fractal 分形体，分维体frequency converter 变频器frequency domain model reduction method 频域模型降阶法frequency response 频域响应full order observer 全阶观测器functional decomposition 功能分解FES (functional electrical stimulation) 功能电刺激functional simularity 功能相似fuzzy logic 模糊逻辑game tree 对策树gate valve 闸阀general equilibrium theory 一般均衡理论generalized least squares estimation 广义最小二乘估计generation function 生成函数geomagnetic torque 地磁力矩geometric similarity 几何相似gimbaled wheel 框架轮global asymptotic stability 全局渐进稳定性global optimum 全局最优globe valve 球形阀goal coordination method 目标协调法grammatical inference 文法推断graphic search 图搜索gravity gradient torque 重力梯度力矩group technology 成组技术guidance system 制导系统gyro drift rate 陀螺漂移率gyrostat 陀螺体Hall displacement transducer 霍尔式位移传感器hardware-in-the-loop simulation 半实物仿真harmonious deviation 和谐偏差harmonious strategy 和谐策略heuristic inference 启发式推理hidden oscillation 隐蔽振荡hierarchical chart 层次结构图hierarchical planning 递阶规划hierarchical control 递阶控制homeostasis 内稳态homomorphic model 同态系统horizontal decomposition 横向分解hormonal control 内分泌控制hydraulic step motor 液压步进马达hypercycle theory 超循环理论I controller 积分控制器identifiability 可辨识性IDSS (intelligent decision support system) 智能决策支持系统image recognition 图像识别impulse 冲量impulse function 冲击函数，脉冲函数inching 点动incompatibility principle 不相容原理incremental motion control 增量运动控制index of merit 品质因数inductive force transducer 电感式位移传感器inductive modeling method 归纳建模法industrial automation 工业自动化inertial attitude sensor 惯性姿态敏感器inertial coordinate system 惯性坐标系inertial wheel 惯性轮inference engine 推理机infinite dimensional system 无穷维系统information acquisition 信息采集infrared gas analyzer 红外线气体分析器inherent nonlinearity 固有非线性inherent regulation 固有调节initial deviation 初始偏差initiator 发起站injection attitude 入轨姿势input-output model 投入产出模型instability 不稳定性instruction level language 指令级语言integral of absolute value of error criterion 绝对误差积分准则integral of squared error criterion 平方误差积分准则integral performance criterion 积分性能准则integration instrument 积算仪器integrity 整体性intelligent terminal 智能终端interacted system 互联系统，关联系统interactive prediction approach 互联预估法，关联预估法interconnection 互联intermittent duty 断续工作制internal disturbance 内扰ISM (interpretive structure modeling) 解释结构建模法invariant embedding principle 不变嵌入原理inventory theory 库伦论inverse Nyquist diagram 逆奈奎斯特图inverter 逆变器investment decision 投资决策isomorphic model 同构模型iterative coordination 迭代协调jet propulsion 喷气推进job-lot control 分批控制joint 关节Kalman-Bucy filer 卡尔曼-布西滤波器knowledge accomodation 知识顺应knowledge acquisition 知识获取knowledge assimilation 知识同化KBMS (knowledge base management system) 知识库管理系统knowledge representation 知识表达ladder diagram 梯形图lag-lead compensation 滞后超前补偿Lagrange duality 拉格朗日对偶性Laplace transform 拉普拉斯变换large scale system 大系统lateral inhibition network 侧抑制网络least cost input 最小成本投入least squares criterion 最小二乘准则level switch 物位开关libration damping 天平动阻尼limit cycle 极限环linearization technique 线性化方法linear motion electric drive 直线运动电气传动linear motion valve 直行程阀linear programming 线性规划LQR (linear quadratic regulator problem) 线性二次调节器问题load cell 称重传感器local asymptotic stability 局部渐近稳定性local optimum 局部最优log magnitude-phase diagram 对数幅相图long term memory 长期记忆lumped parameter model 集总参数模型Lyapunov theorem of asymptotic stability 李雅普诺夫渐近稳定性定理自动控制专业英语词汇（二）macro-economic system 宏观经济系统magnetic dumping 磁卸载magnetoelastic weighing cell 磁致弹性称重传感器magnitude-frequency characteristic 幅频特性magnitude margin 幅值裕度magnitude scale factor 幅值比例尺manipulator 机械手man-machine coordination 人机协调manual station 手动操作器MAP (manufacturing automation protocol) 制造自动化协议marginal effectiveness 边际效益Mason's gain formula 梅森增益公式master station 主站matching criterion 匹配准则maximum likelihood estimation 最大似然估计maximum overshoot 最大超调量maximum principle 极大值原理mean-square error criterion 均方误差准则mechanism model 机理模型meta-knowledge 元知识metallurgical automation 冶金自动化minimal realization 最小实现minimum phase system 最小相位系统minimum variance estimation 最小方差估计minor loop 副回路missile-target relative movement simulator 弹体-目标相对运动仿真器modal aggregation 模态集结modal transformation 模态变换MB (model base) 模型库model confidence 模型置信度model fidelity 模型逼真度model reference adaptive control system 模型参考适应控制系统model verification 模型验证modularization 模块化MEC (most economic control) 最经济控制motion space 可动空间MTBF (mean time between failures) 平均故障间隔时间MTTF (mean time to failures) 平均无故障时间multi-attributive utility function 多属性效用函数multicriteria 多重判据multilevel hierarchical structure 多级递阶结构multiloop control 多回路控制multi-objective decision 多目标决策multistate logic 多态逻辑multistratum hierarchical control 多段递阶控制multivariable control system 多变量控制系统myoelectric control 肌电控制Nash optimality 纳什最优性natural language generation 自然语言生成nearest-neighbor 最近邻necessity measure 必然性侧度negative feedback 负反馈neural assembly 神经集合neural network computer 神经网络计算机Nichols chart 尼科尔斯图noetic science 思维科学noncoherent system 非单调关联系统noncooperative game 非合作博弈nonequilibrium state 非平衡态nonlinear element 非线性环节nonmonotonic logic 非单调逻辑nonparametric training 非参数训练nonreversible electric drive 不可逆电气传动nonsingular perturbation 非奇异摄动non-stationary random process 非平稳随机过程nuclear radiation levelmeter 核辐射物位计nutation sensor 章动敏感器Nyquist stability criterion 奈奎斯特稳定判据objective function 目标函数observability index 可观测指数observable canonical form 可观测规范型on-line assistance 在线帮助on-off control 通断控制open loop pole 开环极点operational research model 运筹学模型optic fiber tachometer 光纤式转速表optimal trajectory 最优轨迹optimization technique 最优化技术orbital rendezvous 轨道交会orbit gyrocompass 轨道陀螺罗盘orbit perturbation 轨道摄动order parameter 序参数orientation control 定向控制originator 始发站oscillating period 振荡周期output prediction method 输出预估法oval wheel flowmeter 椭圆齿轮流量计overall design 总体设计overdamping 过阻尼overlapping decomposition 交叠分解Pade approximation 帕德近似Pareto optimality 帕雷托最优性passive attitude stabilization 被动姿态稳定path repeatability 路径可重复性pattern primitive 模式基元PR (pattern recognition) 模式识别P control 比例控制器peak time 峰值时间penalty function method 罚函数法perceptron 感知器periodic duty 周期工作制perturbation theory 摄动理论pessimistic value 悲观值phase locus 相轨迹phase trajectory 相轨迹phase lead 相位超前photoelectric tachometric transducer 光电式转速传感器phrase-structure grammar 短句结构文法physical symbol system 物理符号系统piezoelectric force transducer 压电式力传感器playback robot 示教再现式机器人PLC (programmable logic controller) 可编程序逻辑控制器plug braking 反接制动plug valve 旋塞阀pneumatic actuator 气动执行机构point-to-point control 点位控制polar robot 极坐标型机器人pole assignment 极点配置pole-zero cancellation 零极点相消polynomial input 多项式输入portfolio theory 投资搭配理论pose overshoot 位姿过调量position measuring instrument 位置测量仪posentiometric displacement transducer 电位器式位移传感器positive feedback 正反馈power system automation 电力系统自动化predicate logic 谓词逻辑pressure gauge with electric contact 电接点压力表pressure transmitter 压力变送器price coordination 价格协调primal coordination 主协调primary frequency zone 主频区PCA (principal component analysis) 主成分分析法principle of turnpike 大道原理priority 优先级process-oriented simulation 面向过程的仿真production budget 生产预算production rule 产生式规则profit forecast 利润预测PERT (program evaluation and review technique) 计划评审技术program set station 程序设定操作器proportional control 比例控制proportional plus derivative controller 比例微分控制器protocol engineering 协议工程prototype 原型pseudo random sequence 伪随机序列pseudo-rate-increment control 伪速率增量控制pulse duration 脉冲持续时间pulse frequency modulation control system 脉冲调频控制系统pulse width modulation control system 脉冲调宽控制系统PWM inverter 脉宽调制逆变器pushdown automaton 下推自动机QC (quality control) 质量管理quadratic performance index 二次型性能指标qualitative physical model 定性物理模型quantized noise 量化噪声quasilinear characteristics 准线性特性queuing theory 排队论radio frequency sensor 射频敏感器ramp function 斜坡函数random disturbance 随机扰动random process 随机过程rate integrating gyro 速率积分陀螺ratio station 比值操作器reachability 可达性reaction wheel control 反作用轮控制realizability 可实现性,能实现性real time telemetry 实时遥测receptive field 感受野rectangular robot 直角坐标型机器人rectifier 整流器recursive estimation 递推估计reduced order observer 降阶观测器redundant information 冗余信息reentry control 再入控制regenerative braking 回馈制动，再生制动regional planning model 区域规划模型regulating device 调节装载regulation 调节relational algebra 关系代数relay characteristic 继电器特性remote manipulator 遥控操作器remote regulating 遥调remote set point adjuster 远程设定点调整器rendezvous and docking 交会和对接reproducibility 再现性resistance thermometer sensor 热电阻resolution principle 归结原理resource allocation 资源分配response curve 响应曲线return difference matrix 回差矩阵return ratio matrix 回比矩阵reverberation 回响reversible electric drive 可逆电气传动revolute robot 关节型机器人revolution speed transducer 转速传感器rewriting rule 重写规则rigid spacecraft dynamics 刚性航天动力学risk decision 风险分析robotics 机器人学robot programming language 机器人编程语言robust control 鲁棒控制robustness 鲁棒性roll gap measuring instrument 辊缝测量仪root locus 根轨迹roots flowmeter 腰轮流量计rotameter 浮子流量计，转子流量计rotary eccentric plug valve 偏心旋转阀rotary motion valve 角行程阀rotating transformer 旋转变压器Routh approximation method 劳思近似判据routing problem 路径问题sampled-data control system 采样控制系统sampling control system 采样控制系统saturation characteristics 饱和特性scalar Lyapunov function 标量李雅普诺夫函数SCARA (selective compliance assembly robot arm) 平面关节型机器人scenario analysis method 情景分析法scene analysis 物景分析s-domain s域self-operated controller 自力式控制器self-organizing system 自组织系统self-reproducing system 自繁殖系统self-tuning control 自校正控制semantic network 语义网络semi-physical simulation 半实物仿真sensing element 敏感元件sensitivity analysis 灵敏度分析sensory control 感觉控制sequential decomposition 顺序分解sequential least squares estimation 序贯最小二乘估计servo control 伺服控制，随动控制servomotor 伺服马达settling time 过渡时间sextant 六分仪short term planning 短期计划short time horizon coordination 短时程协调signal detection and estimation 信号检测和估计signal reconstruction 信号重构similarity 相似性simulated interrupt 仿真中断simulation block diagram 仿真框图simulation experiment 仿真实验simulation velocity 仿真速度simulator 仿真器single axle table 单轴转台single degree of freedom gyro 单自由度陀螺single level process 单级过程single value nonlinearity 单值非线性singular attractor 奇异吸引子singular perturbation 奇异摄动sink 汇点slaved system 受役系统slower-than-real-time simulation 欠实时仿真slow subsystem 慢变子系统socio-cybernetics 社会控制论socioeconomic system 社会经济系统software psychology 软件心理学solar array pointing control 太阳帆板指向控制solenoid valve 电磁阀source 源点specific impulse 比冲speed control system 调速系统spin axis 自旋轴spinner 自旋体stability criterion 稳定性判据stability limit 稳定极限stabilization 镇定，稳定Stackelberg decision theory 施塔克尔贝格决策理论state equation model 状态方程模型state space description 状态空间描述static characteristics curve 静态特性曲线station accuracy 定点精度stationary random process 平稳随机过程statistical analysis 统计分析statistic pattern recognition 统计模式识别steady state deviation 稳态偏差steady state error coefficient 稳态误差系数step-by-step control 步进控制step function 阶跃函数stepwise refinement 逐步精化stochastic finite automaton 随机有限自动机strain gauge load cell 应变式称重传感器strategic function 策略函数strongly coupled system 强耦合系统subjective probability 主观频率suboptimality 次优性supervised training 监督学习supervisory computer control system 计算机监控系统sustained oscillation 自持振荡swirlmeter 旋进流量计switching point 切换点symbolic processing 符号处理synaptic plasticity 突触可塑性synergetics 协同学syntactic analysis 句法分析system assessment 系统评价systematology 系统学system homomorphism 系统同态system isomorphism 系统同构system engineering 系统工程tachometer 转速表target flow transmitter 靶式流量变送器task cycle 作业周期teaching programming 示教编程telemechanics 远动学。
税控型号单词
税控型号单词单词：Tax - control model1. 定义与释义1.1词性：名词1.2释义：税控型号，是指用于控制和管理税收相关事务的设备或软件的特定型号。
1.3英文解释：A specific type of equipment or software used for controlling and managing tax - related affairs.1.4相关词汇：Tax - control（税控），model number（型号）2. 起源与背景2.1词源：随着现代税收制度的发展，为了更精准地管理税收，对税收相关设备和软件进行分类编号，从而产生了“税控型号”这个概念。
“tax”源于古英语“taxian”，表示指责、征税等意思；“control”源于中古英语“controllen”，表示控制；“model”源于古法语“modelle”，表示模型、样式。
2.2趣闻：在一些国家的税收改革过程中，新的税控型号设备的推出往往伴随着一系列的政策调整和企业适应过程。
例如，某国推出新型税控收款机型号时，一些小商户因为不熟悉操作，需要花费额外的时间和精力来学习如何使用，这期间也引发了很多关于如何更好地推广新税控设备的讨论。
3. 常用搭配与短语3.1短语：(1) Tax - control model installation：税控型号安装例句：The tax - control model installation should be carried out by professional technicians.翻译：税控型号的安装应该由专业技术人员来进行。
(2) Update of tax - control model：税控型号更新例句：The update of tax - control model is necessary to keep up with the latest tax policies.翻译：为了跟上最新的税收政策，税控型号的更新是必要的。
自动化英文文献
Classification of control systems there are three ways: by automatic classification methods in order to participate in the control mode classification, to adjust the law category.One way to control category1, the open-loop control system if the computer output of open loop control system to exercise control of the production process, but the control results --- the state of the production process does not affect the computer control systems, computer \ controller \ production and other sectors does not constitute a closed loop, is called open-loop control system computer. the production process of the state is no feedback to the computer, but by the operator to monitor the status of the production process, decision control program, and tell the computer to control the role of exercising control.2, closed loop control system computer to the production of an object or process control, the state can directly influence the production process computer control system, called the closed-loop control system computer. Control of the computer monitor in the operator, the automatic acceptance of the production process state test results, calculate and determine the control scheme, the direct command and control units (devices) of action, the role of exercising control of the production process. In such systems, aircraft control components under control of control information sent to control device operation, the other running equipment condition as the output, measured by the detection part, the feedback as input to the control computer; to make control Computer \ Control Components \ production \ test components form a closed loop. We will call this form of control computer control closed-loop control. Closed loop control system computer, using a mathematical model to set the value of the production process and test results of the best value of the deviation between the feedback and control the production process to run at their best.3, line control system as long as the computer controlled production of the controlled object or process, to exercise direct control, without human intervention are called the control computer on-line control, or on-line control system.4, offline control system control computer does not directly participate in the control object or the controlled production process. It only managed to complete the process of the controlled object or the status of testing, and testing of data processing; and then develop control programs, the output control instruction, operator reference control instructions manually controlled operation to control parts of the object orsubject control process control. This control form is called off-line computer control system.5, real-time control system control computer real-time control system is controlled by the control of the object or process, or request when the request processing control, the control function in a timely manner to address and control systems, commonly used in the production process is interrupted for the occasion. Such as steel, each one refining furnace steel is a process; and if the process rolling, rolling out each piece of steel considered a process, each process is repeated. Only enter the process only requires a computer control. Once control of the computer, it requires a computer from the production process information in the required time to respond to or control. Such systems often use sophisticated interrupt system and interrupt handling procedures to achieve. In summary, an online system is not necessarily a real-time system. But a real-time system must be an online system.Second, in order to participate in the control mode to Category1, direct digital control system by the control computer to replace conventional analog instruments and direct regulation to control the production process, as the computer as digital signals, so named after the DDC control. Actually controlled the production process control components, control signals received by the process controller input / output channels of D / (D / A) converter output of the digital control computer volume to be converted into analog; analog input control machine to go through the process of input / output channels of analog / digital (A / D) converter into a digital number into the computer. DDC control systems often use a small computer or microprocessor, the time-sharing system to achieve multiple points of control. Is in fact a discrete sampling with the controller, to achieve discrete multi-point control. DDC computer control system that has become the main control computer control system forms. DDC control of the advantage of flexibility, large, focused on high reliability and low cost. Can use several forms of digital computing circuits, or even dozens of loop production process, integral to proportional --- --- differential (PID) control to maintain the industrial state of the controlled object at a given value, the deviation small and stable. And as long as the change of control algorithms and applications can achieve more complex control. Such as feedforward control and the best control. Under normal circumstances, DDC-level control often more complex as the implementation of advanced control level.2, supervisory computer control system supervisory computer control system fora particular production process, according to the production process of various states, according to the production process of the mathematical model to calculate the best production equipment should be running a given value, and the best value automatically or manually on the DDC Executive-level computer or analog meter to align the regulation or control of the target set. By a DDC or adjust the instrument at various points on the production process (running equipment) to exercise control. SCC system is that it can guarantee the production process is always controlled the situation in the best condition to run, so get the most benefit. SCC results directly affect the merits of the first of its mathematical model, this should always improve the operation process model, and modify the control algorithm, and application control procedures.3, multi-level control systems in modern manufacturing enterprises in the production process not only the need to address the problem of online control, and Huan Zhi Li called for a solution of production problems, the daily product line, the number of arrangements for planning and scheduling, and Rose plans develop a long term planning, notice Xiaoshou prospects, there was multi-level control system. DDC class is mainly used for direct control of the production process, for PID, or feedforward control; SCC level is mainly used for optimal control or adaptive control or learning control calculation, and command and control the same DDC class report back to the MIS class. DDC level usually microcomputers, SCC-level general use of small computers or high-end microcomputers. MIS Workshop main function of governance is based on plant-level production of varieties issued, the number of orders and collect up the production process of the state of information, at any time reasonable schedule to achieve optimal control, command and SCC-level supervisory control. Factory management level MIS main function is to accept the company and factory production tasks assigned by the actual situation of optimized computing, Zhi Ding factory production plans and short-term (ten days or weeks or days) arrangements, and then issued to the plant-level production tasks. Corporate governance level MIS main function is to guess the market demand computing to develop strategic long-term development planning, and contract orders, raw material supply situation and the production conditions, comparison of the optimal production program selection and calculation, work out the entire company business a long time (months or ten days) of the production plan, sales plan, assigned to the task of the factory management level. MIS-level main function is to achieve real-timeinformation processing, decision-makers at all levels to provide useful information, make on the production planning \ scheduling and management programs to plan the coordination and management control in the optimal state. This one can control the size and scope of enterprise size divided into several levels. Each level has to be addressed according to the size of the amount of information to determine the type of computer used. MIS generally use small computer shop class or high-grade micro-computer, the factory management level of the MIS with a medium-sized computer, and corporate governance level MIS is to use large-scale computer, or use super computer. 4, distributed control or distributed control system distributed control or distributed control, the control system is divided into a number of independent local control subsystems to complete the controlled production process control task. Since the emergence of micro-computers and rapid development of distributed control to provide for the realization of the material and technical basis, in recent years, decentralized control can be different almost normal development, and has become an important trend in the development of computer control. Since the 70's, appeared focused on distributed control system, called DCS. It is a decentralized local control of the new computer control system.Three, classified according to the law regulating1, program control if the computer control system the division of a predetermined time function control, such control is called program control. Such as the furnace temperature-time curves Anzhao some control on the process control. Here the procedure is time-varying changes have to determine the corresponding value, rather than the computer running.2, sequence control in the process control based on the generated sequence control, computer, over time, as can be determined according to the corresponding control value and previous results at the moment both to exercise on the production process control system, called the order of the computer control .3, proportional - integral - differential analog PID control regulation of conventional PID control instrument can be completed. Micro-computer can also be achieved with PID control.4, feedforward control is usually the feedback control system, have certain effects on the interference in order to generate feedback over the role of inhibitory control of interference, and thus delay the control of undesirable consequences. In order to overcome the negative lag control, with the computer accepts the interferencesignal after the, did not produce effects in the Huan insert a feedforward control Zuoyong, it Ganghao interference point in the interference of the control to completely offset the effect on the variable, it was Ming Wei Yin Er disturbance compensation control.5, optimal control (optimal control) system control computer, such as to have controlled object is best known as the best run of the control system control system. Such as computer control system is limited in the existing conditions, select appropriate control law (mathematical model), the controlled object indicators in optimal running condition. Such as the largest output, consumption of the largest, highest quality standards, such as the least scrap rate. Best is determined by a set of mathematical models, sometimes several in a limited range of the best indicators of the pursuit of individual, sometimes the best indicators of comprehensive requirements.6, the adaptive control system, optimal control, when the working conditions or qualifications change, we can not get the best control effects. If the situation changes in working conditions, the control system can still be controlled in the best state of the object's control, such control system called the adaptive system. This requires mathematical model reflects the change in the conditions, how to achieve the best state. Control computer to detect changes in terms of the information given by the laws of mathematical models to calculate, to change the control variables, the controlled objects still in the best condition.7, self-learning control system if the computer can keep the results under the controlled object gain experience running their own change and improve the control law so that more and better control effect, this control system is called self-learning control system. Above mentioned optimal control, adaptive control and self-learning control are related to multi-parameter, multi-variable complex control systems, are all problems of modern control theory. Determine the stability of the system, many factors affect the control of complex mathematical models, have to be a production control, production technology, automation, instrumentation, programming, computer hardware, each with various personnel to be realized. Controlled object by the length of reaction time required to control the number of points and mathematical models to determine the complexity of the computer use scale. Generally speaking, a strong need to functionality (speed and computing power) of the computer can be achieved. The Zhuzhong control, can be a single type also is not single, you can combineseveral forms to achieve control of the production process. This should address the actual situation of the controlled object, the system analysis, system design determined at the time.。
牵引车-飞机系统的路径跟踪控制（英文）
J.Marine Sci.Appl.(2012)11:512-517DOI:10.1007/s11804-012-1162-xPath-tracking Control of a Tractor-aircraft SystemNengjian Wang,Hongbo Liu*and Wanhui YangSchool of Mechanica l and Electr ical Engineering，Har bin Engineering Univer sity,Ha rbin150001,ChinaAbstra ct:An aircraft tractor plays a significant role as a kind of important marine transport and supportequipment.It’s necessary to study its controlling and manoeuvring stability to improve operation efficiency.Avirtual prototyping model of the tractor-aircraft system based on Lagrange's equation of the first kind withLagrange mutipliers was established in this paper.According to the towing characteristics,a path-trackingcontroller using fuzzy logic theory was designed.Direction control herein was carried out through acompensatory tracking approach.Interactive co-simulation was performed to validate the path-trackingbehavior in closed-loop.Simulation results indicated that the tractor followed the reference courses preciselyon a flat ground.Keywords:path-tracking controller;aircraft tractor;preconcert route;fuzzy control;co-simulationArticle ID:1671-9433(2012)04-0512-061Introduction1Automatic guidance of industrial articulated vehicles,such as mining trucks,earth-removal and road-paving vehicles, intercity bus travels,and automated guided vehicles(AGVs), (Lane et al.,1994;Larsson et al.,1994;Hirose et al.,1995; Rabinovitch and.Leitman,1996;de Santis,1997;Lamiraux et al.,1999);have over80years,received a great deal of attention from researchers.Recently,a study in intelligent control technology for maritime applications has prompted more research investigating.For more than20years the study of tractor aircraft systems has provided vital information for on researching maritime vessel transportation.The process has been noted as to being a complicated nonholonomic, under-actuated and nonlinear system.The path-tracking plays a significant role in improving operation efficiency(Rifford, 2004,2006,2008;Nakamura et al.,2001).Wang(1994) Aircraft tractors are essential tools for aircraft movement on large ships,as well as takeoff and landing.The mechanism is different from a shore-based allocation and transporting of an aircrafts;tractors on the ship are placed in less than ideal environments,narrow space and exclusive transportation facilities by Han et al.(2010).Relatively good transport efficiency and flexibility are required during these tasks.As a result,the lack of maneuverability has increased a higher rate of involvement in fatal accidents.Through constant evolution and development of computer and sensor technologies,research on tracking control methods for two-wheeled and car-like mobile robots have increased significantly(de Wit et al.,1993;Kanayama et al.,1990,1991; Murray and Sastry,1993;Samson and Ait-Abderrahim,1991a, Received d at e:2011-11-13.Found at ion item:Harbin Technological Innovation ResearchFund(NO:2012RFXXG039)*C orresp ond ing aut hor Email:lhbci************©Harbin Engineering Univers ity and Springer-Verlag Berlin Heidel berg20121991b).In addition,a few researchers have explored in greater detail the study of tracking control of trailer systems,which basically consist of a steering tractor and a passive trailer, linked with a rigid joint,such as a tractor-aircraft system.As noted in the references listed:(Lamiraux and Laumond,1997; Sekhavat et al.,1997;Yuan and Huang,2006)much of the interest driving experimentation,is the utilization of trailers on mobile robots.However,problems occur due to the controlling of the system from the viewpoint of the mobile robot and not a passive trailer.In1994,de Santis,conducted a simple linear control study using a linearized model designed for a trailer system.The research is of great interest and a positive perspective on the study of tracking control systems guide points have been explored for future recommendations.The study was divided into three components:First, analyzing the tractor aircraft systems,examining the marine transport equipment,and understanding the procedures of the maneuvering stability of a ship.Next,the research focused on guiding a path tracking controlled aircraft tractor into preconcerted routes and keeping a smooth motion, almost like a flat ground on a ship.Thirdly,the paper focused on analyzing the performance of the tractor in an automatic navigation system setting.The research study utilized the fuzzy logic theory as a measuring tool in the designing of the controller for the tractor-aircraft system.The researcher also took into account factors for the adverse ef fects,caused by factors such as tire slippage.The direction control was performed through a compensatory tracking approach method.The organizational flow of the research paper has been divided into five sections.In section II,the research focused on the kinematic and virtual prototyping model of the aircraft-tractor system.Section III,focused on theJournal of Marine S cience and Appl ication (2012)11:512-517513design of the fuzzy control system,while section IV contains simulation results.The paper concludes with remarks and recommendations in section V .2Model of a tr actor-aircraft system2.1Kinematic mod elThe model is based on a rigid multiply body that consists of a tractor,a drawbar,the undercarriage and fuselage,ignoring,for the moment,the flexibility of the tractor suspension and undercarriage buffer system.It is usually assumed that the wheels do not slip.The deformation of the tires is also ignored for the sake of simplicity.These assumptions are acceptable for tractor towing at low speeds:(1)Calculate the lateral component of constraint force onthe tractor-aircraft system junction.(2)The relative angles between the various parts are small,and the tractor front wheel steering angle is small.(3)Examine the wheels rolling resistance,back torque and air resistance.Primarily consider the lateral and the swaying motions of the tractor-aircraft system illustrated in Figure1.Fig.1Kinematic model of a tractor-aircraft system Dynamical equations of the tractor are shown as the following.11111121()cosy a y y m v u r F F F R (1)111122131cos z y y y a J r F d F d R d M (2)The drawbar and the nose landing gear dynamical equations are depicted as:2222323()y y y m v u r R R F (3)22243535z y y y J r R d R d F d (4)Dynamical equations of the fuselage and the rear landinggear are founded with the expression3333244()a y y m v u r F F R (5)3346247z y a y J r R d M F d (6)where i m represents the mass (the subscript i=1,2,3denotes the tractor,the drawbar and the aircraft respectively),i u is the marching velocity,i r is the sway rate and yi F is the cornering force on the tractor wheel,yi R is the lateral constraint reacting force on the articulation and the vertical moment of inertia is expressed with iz J ,ai F and ai M are the accessional lateral force and torque on the centroid,δis the tractor front wheel steering angles,12,d d are the distances from the tractor centroid to the front and rear axle,3d is the distance from the tractor centroid to the anterior to the drawbar,45,d d are the distances from the drawbar centroid to its foreside and rearward,67,d d are thedistances from the aircraft centroid to the front and rear axle.Cornering force on the tractor wheels yi F is defined as a function of the slip angle.When the lateral acceleration is less than 0.4g,the slip angle is generally no more than 4°-5°,the tire cornering properties are in the linear range.Cornering force is given by y i i i F k a ,where i k is thecornering stiffness,its value is negative,i a is the tire slipangle.The state equation of tractor-aircraft towing operation can be described by means of:K XL XM UN TS F(7)2.2Vir tual prototyping modelUsing the ADAMS/View program(Elliott,2000),a virtual prototyping model is created as shown in Figure 2.A centralized quality tractor model is established,which includes the body,suspension and steering system,tires and other components.The study shows evidence of a reduction in the drawbar to a cylindrical rod..The aircraft model is mainly composed of the fuselage,undercarriage and employs spring-dampers.As a result,nonlinear elastic damping effects in the spline curve takes place in the undercarriage buffer system.Nengjian W ang,et al.Path-tracking C ontrol of a T ract or-A ircraft System514Fig.2Virtual prototyping model of the tractor-aircraftsystemThe parameters of the tire and road can be set in the Fiala tyre model and mdi_2d_flat road model,such as:the vertical stiffness,vertical damping of the tire,the friction factor ,and graphics of the road.2.3Comparative analysis of kinetic model an d virtualprototype mod elA comparative analysis was conducted to set the tractor initial position on the ground coordinate system origin and zero degree for the initial direction.The simulation was carried out using a vertical speed of 5km/h.The step input was given to a steering wheel with the function:step (time,8,0,8.02,and 42d).The study compared the steady-state values of the kinetic model and the virtual prototype model,as shown in Table 1.It was established that the virtual prototype model is a good feature.Table 1Contrast of the Kinetics ParametersInvestigating variablesY aw rate of the tractor/((°)·s 1)Yaw rate of thedrawbar/((°)·s 1)Y aw rate of the aircraft/((°)·s 1)Lateral velocityof the tractor/(mm/s)Angle between the drawbar and the aircraft/(°)Angle betweenthe tractor and the drawbar/(°)Simulation value1.4361.4271.40445.904.1266.222Theoretic value 1.507 1.507 1.47846.60 4.395 6.549Absolute error 0.0740.0800.0740.7000.2690.327Relative error5.3%5.6%5.3%1.5%6.5%5.2%3Establishment of fuzzy control sysytemBased on the virtual prototype model of the tractor-aircraftsystem a Mamdani fuzzy control system is established (Shukla and Tiwari,2010).A block diagram of the fuzzy control system is visible in Figure 3.Distance deviation and angle deviation,which can be derived by drawing acomparison between the actual path,and the preconcerted routes are calculated as the input of the controller.The torque that controls the steering wheel angle sheers off betimes to eliminate the error is referred to as the output.Fig.3Block diagram of the controller3.1Path Reference fr ameUbiety between the tractor and the preconcerted route is shown in Figure4.The ground coordinate system OX YZ is used to describe the trajectory,whereas vehicle coordinated system oxyz is used to calculate the distance deviation Ed and angle deviation Ea.Path point P c (c=1,2,3,…n)connecting to the sequentially composed preconcerted path.The origin of the vehicle system of coordinates is (X 0，Z 0)on the ground coordinate and the relative angle between these two coordinated systems is .Fig.4Schematic diagram of the ubiety3.2Posit ion ControllerJournal of Marine S cience and Appl ication (2012)11:512-517515The functions of the fuzzification interface are to perform the following steps:measure the values among the input variables from the data acquisition interface,quantifying in order to transform the range of the observed values into the corresponding discourse of the language variables,and transforming the input data into proper linguistic values,that can be regarded as a form of fuzzy set.The subets of the in-out variables are decomposed into seven fuzzy partitions,denoted by PB (positive big),PM (positive medium),PS (positive small),Z (zero),NS (negative small),NM (negative medium),and NB (negative big),respectively.The domain of distance deviation,Ed is [–1000,1000],Unit:mm and of angle deviation Ea is [1.57,1.57],Unit:rad.Control axial torque on the steering wheel has a basic domain of [78400,78400]which unit is N ·mm.In-out variables in fuzzy set are on the fuzzy domain {6,4,2,0,2,4,6}.Analyzing the basic domain and the compartmentalization of the hierarchy,quantization factor of distance deviation Kd comes to a value of 0.006and that of angle deviation Ka is 0.267,while the control torque scale factor Kt is 13066.The membership function of in-out is shown in Figure5.Fig.5Membership functionThe rule table of fuzzy controller is shown in Table 2and the output surface of fuzzy control rules can be illustrated as shown in Figure6.There are four conditions of the tractor current position and preconcerted route determined by the distance and angle deviation:(a)0,0Ed Ea ;(b)0,0Ed Ea ;(c)0,0EdEa;(d)0,0EdEaTable 2Rule table of fuzzy controllerOutput Torque UEd NB NM NS Z PS PM PB Ed NB PB PB PB PM NS NS NS NM PB PB PM PS NS NS NS NS PB PM PM PS NS NS NS ZPM PM PM Z NM NM NM PS PS PS PS NS NM NM NB PM PS PS PS NS NM NB NB PBPSPSPSNMNBNBNBEd EaFig.6Output surface of fuzzy rules4Tracking behavior simulation analysisFor verifying the efficiency of the proposed controller,we realize this system on the virtual prototyping model created in section Ⅱ.Define the in-out adopting ADAMS/Controls and establish the control algorithms in Simulink Model.The study implemented control modules and designed software in the control system,and interactive simulation.The co-simulation model is shown in Figure7,which contains dynamic modules;path deviation calculation module,a fuzzy control module and a time limit module.The corresponding oscilloscope to record the distance and angle deviation and other important data were also established.Thus,the operations and some experimental results are presented in a series of pictures to demonstrate the efficiency of the proposedmethods.Fig.7Co-simulation mode l4.1Performance of the Virtu al Prototyping ModelGiven the tractor rear wheel,a axial torque with a step input:step (time,0,50000,180,1800000)and a drive function to the steering wheel with:step (time,0,0,1,168d),the simulation was carried out.The traction trajectory is shown in Figure 8.The simulations illustrated in Figs.7and 8,results indicate that the under-steer system increased the tractor turningNengjian W ang,et al.Path-tracking Control of a T ractor-A ircraft System 516radius and lateral velocity.t.The tractor's turning radius andlateral velocity are greater than those of the aircraft.Aforesaid analysis proves that the virtual prototyping modelhas good maneuveringstability.(a)Route of the Idle Load Tractor(b)Route of the Load-CarryingTractor(c)Route of the Passive AircraftFig.8TractionTrajectoryFig.9Turning Radius of theTractorFig.10Lateral Velocity Comparison4.2Tr acking Beh avior Und er th e Fuzzy Con trolTowing the aircraft at the speed of5.4km/h along route1(visible in Fig.11),simulations was carried out as follows:(a)Running with a step input:step(time,4,0,4.2,42d)(b)Control the system through co-simulation approachWe investigate the performance of the fuzzy control system.Figure12shows the tracking behavior under an operation ofclosed-loop input.The foundation of the fuzzy controllercould make up some adverse effects caused by tire slippage,etc,to a certain extent.Also the establishment plays animportant role in safe and efficient towingoperation.Fig.11PreconcertedRoutesFig.12Tracking TrajectoriesThe tractor drove in accordance with the intended route2asshown in Figure13,pulling the aircraft from point A todestination B at the speed of 5.4km/h.The trackingtrajectories also obtained the kinetics parameters during thetask from the co-simulation.Figure12shows the lateralvelocity and turn angle of the aircraft for wheel values.Themaximum kinetics parameters are also shown in Tab.4characterizing the towing performance.Therefore,using the designed controller to guide thetraction system tracking in an intended route under practicaltraction work conditions issafe.(a)L ateral Velocity of theTractor(b)Turn Angle of the Aircraft Fore-wheelFig.13Kinetics ParametersJournal of Marine S cience and Appl ication(2012)11:512-5175175ConclusionsFor the automatic guidance and stability control of the ship-based tractor-aircraft system,a fuzzy control system was designed.Firstly,taking into account lateral and the swaying motions,a nonlinear dynamic model is introduced.A virtual prototyping model,which has good maneuvering stability,is established.Furthermore,based on the fuzzy logic,the controller is derived based on the virtual prototyping model.The simulation results confirm the fuzzy control system effectively enables the traction system to track the preconcerted path well.Under the control of the designed controller,the tractor-aircraft system provided a good description of the dynamic behavior.ReferencesDe Santis RM(1994).Path-tracking for a tractor-trailer-like robot.Int J Robot Res,13(6),533-543.De Santis R(1997).Modeling and path-tracking for a load-haul-dump vehicle.J.Dynam.Sy st.Mea s.Contr.,119, 40-47.De Wit C,Khennouf H,Samson C,Sordalen OJ(1993).Nonlinear control design for mobile robots,recent trends in mobile robots.World Scientific Series in Robotics and A utomated Systems,11, 121-156.Elliott AS(2000).A highly efficient,general purpose approach for cosimulation with ADAMS.MDI North A mer,User Conf.,MI,. Han F,Yang BH,Wang HD,Bi YQ(2010).The optimizing research on aircraft handling workflow.Science Technologya nd Engineer ing,10(22),5602-04.Hirose S.Fukushima E,Tsukagoshi S(1995).Basic steering control methods for the articulated body mobile robot.IEEE Contr.Syst.Mag.,4,5-14.Kanayama Y,Kimura Y,Miyazaki F,Noguchi T(1990).A stable tracking control method for an autonomous mobile robot.IEEE Int Conf on Robotics and A utomation,Cincinnati,OH, 384-389.Kanayama Y,Kimura Y,Miyazaki F,Noguchi T(1991).A stable tracking control method for a non-holonomic mobile robot.Int Conf on Intelligent Robotics Systems,Osaka,Japan,1236-1241. Lamiraux F,Laumond JP(1997).A practical approach to feedback control for a mobile robot with trailer.IEEE Int Conf on Robotics a nd A utoma tion,leuv en,Belgium,3306-3311. Lamiraux F,Sekhavat S,Laumond J(1999).Motion planning and control for Hilare pulling a trailer.IEEE Tra ns.Robot.Automa t, 15,640-652.Lane J,King R(1994).Computer-assisted guidance of an underground mine truck.IEEE Int.Conf.Robotics a nd Automation,San Francisco,420-425.Larsson U,Zell C,Hyppa K,Wernesson A(1994).Navigating an articulated vehicle and reversing with a trailer.IEEE Int.Conf.Robotics a nd A utoma tion,San Francisco,2398-2404.Murray RM,Sastry S(1993).Nonholonomic motion planning: Steering using sinusoids.IEEE T r ans A utomat Contr,38(5), 700-716.Nakamura Y,Ezaki H,Tan Y,Chung W(2001).Design of steering mechanism and control of nonholonomic trailer systems.IEEE Transactions on Robotics and A utomation,17(3),367-374. Rabinovitch J,Leitman J(1996).Urban planning in Curitiba.Sci.A mer.,274(3),46-53.Rifford L(2008).Stabilization problem for nonholonomic control systems.Geometr ic Contr ol and N onsmooth A nalysis,Series on A dvances in Mathematics for A pplied Sciences,76,260-269.Rifford L(2006).The stabilization problem on surfaces.Control Theory a nd Stabilization II,64(1),55-61.Rifford L(2004).The stabilization problem:AGAS and SRS feedbacks.Optimal Control,Stabilization,and Nonsmooth Analysis.L ectures N otes in Control a nd Information Sciences, 301,173-184.Samson C,Ait-Abderrahim K(1991a).Feedback stabilization of a nonholonomic wheeled mobile Robot.Int Conf on Intelligent Robotics Systems,1242-1247.Samson C,Ait-Abderrahim K(1991b).Feedback control of a nonholonomic wheeled cart in cartesian space.IEEE Int Conf on Robotics and Automation,1136-1141.Sekhavat S,Lamiraux F,Laumond JP,Bauzil G,Ferrand A(1997).Motion planning and control for Hilare pulling a trailer.IEEE Int Conf on Robotics and A utomation,L euven,Belgium, 3306-3311.Shukla S,Tiwari M(2010).Fuzzy logic of speed and steering control system for three dimensional lines following of an autonomous vehicle.Inter national Journa l of Computer Science and Information Security,7(3),101-108.Wang Y(1994).Development of aircraft-towing tractor.Inter-national A viation,11(9),18-20．Yuan J,Huang YL(2006).Path following control for tractor-trailer mobile robots with two kinds of connection structures.IEEE/RSJ International Conference on Intelligent Robots and Systems,Beijing,China,2533-2538.Nengjian Wang was born in1962.He has been aprofessor at Harbi n Engineering University since2003.He has been a s upervisor for decades.Hisresearch covers a wide range of problems inmodern manufact uring systems theory,workshopand logistics scheduling and optimization,comput er-aided process planning and mechanicaldynamics.Hongbo Liu was born in1987.She is working ondoctoral degree at Harbin Engineering University.She mainly engages in computer simul ation,anal ysis of ai rcraft tracti on system dynamics andstabilit y control study.。
系统的能控性和能观性 英文版
Unit 13 Controllability and ObservabilityA system is said to be controllable at time 0t if it is possible by means of an unconstrained control vector to transfer the system from any initial state )(0t x to any other state in a finite interval of time. A system is said to be observable at time 0t if, with the system in state )(0t x , it is possible to determine this state from the observation of the output over a finite time interval.The concepts of the controllability and observability were introduced by Kalman. They play an important role in the design of control systems in state space. In fact, the conditions of controllability and observability may govern the existence of a complete solution of the control system design problem. The solution to this problem may not exist of the system considered is not c ontrollable. Although most physic al systems are c o ntrollable and observable, corresponding mathematical models may not possess the property of controllability and observability.Complete State Controllability of Continuous-Time SystemsConsider the continuous-time systemBu AX X+= (13. 1) where X=state vector (n -vector)u =control signal (scalar) A=n n ⨯ matrix B=1⨯n matrixThe system described by Equation (13. 1) is said to be state controllable at 0t t =if it is possible to construct an unconstrained control signal that will transfer an initial state to any final state in a finite time interval 10t t t ≤≤. If every state is controllable, then the system is said to be completely state controllable.We shall now derive the condition for complete state of controllability. Without loss of generality, we can assume that the final state is the origin of the state space and that the initial time is zero,or 00=t .The solution of Equation (13. 1) is⎰-+=tt A Atd Bu eX e t X 0)()()0()(τττApplying the definition of complete state controllability just given, we have⎰-+==111)(1)()0(0)(t t A At d Bu eX et X τττor⎰--=10)()0(t A d Bu eX τττ(13. 2)And τA e -can be written∑-=-=1)(n k kkA A e τατ(13. 3)Substituting Equation (13. 3) into Equation (13. 2) gives∑⎰-=-=101)()()0(n k t k kd u B A X τττα (13. 4)Let us put⎰=1)()(t k k d u βττταThen Equation (13. 4) becomes∑-=-=1)0(n k k kB A X β[]⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡=--1101n n B A AB Bβββ(13. 5) If the system os completely state controllable, then, given any initial state X(0), Equation(13. 5) must be satisfied. This requires that the rank of the n n ⨯matrix[]B AAB Bn 1-be n .From this analysis, we can state the condition for complete state controllability as follow s. The system given by Equation (13. 5) is completely state controllable if and only if the vectorsB AAB B n 1,- are linearly independent, or the n n ⨯matrix[]B AAB Bn 1-is the rank n.The result just obtained can be extended to the case where the control vector U is r-dimensional. If the system is described byBU AX X+= Where U is an r -vector, then it can be proved that the condition of for complete statecontrollability is that the n n ⨯matrix[]B AAB B n 1-be of rank n , or contain n linearly independent column vectors. The matrix[]B AABBn 1-is commonly called the controllability matrix.Complete Observability of Continuous-Time SystemIn this section we discuss the observability of linear systems. Consider the unforced system described by the following equationsAX X= (13. 6) CX Y = (13. 7)where X=state vector (n -vector)Y=output vector (m -vector) A=n n ⨯matrix C=n m ⨯matrixThe system is said to be completely observable if every state )(0t X can be determined from the observation of Y(t) over a finite time interval,10t t t ≤≤. The system is, therefore, completely observable if every transition of the state eventually affects every element of the output vector. The concept of observability os useful in solving the problem or reconstructing unmeasurable state variable from measurable variables in the minimum possible length of time. In this section we treat only linear, time-invariant systems. Therefore, without loss of generality, we can assume that 00=t .The concept of observability is very important because, in practice, the difficulty encountered with state feedback control is that some of the state variables are not accessible for direct measurement, with the result that it becomes necessary to estimate the unmeasurable state variables in order to construct the control signals.● Such estimates of state variables are possible of and only if the system is completely observable.In discussion observability conditions, we consider the unforced system as given by Equation (13. 6) and (13. 7). The reasons for this are as follows, If the system is described byBu AX X+= Bu CX Y +=then⎰-+=tt A Atd Bu eX e t X 0)()()0()(τττAnd Y(t) is⎰++=-tt A AtDu d Bu eC X Cet Y 0)()()0()(τττSince the matrices A, B, C, and D are known and u(t) is also known,the last terms onthe right-hand side of this last equation are known quantities. Therefore, they may be subtracted from the observed value of Y(t). Hence, for investigating a necessary and sufficient condition for complete observability, it suffices to consider the system described by Equations (13. 6) and (13. 7).Consider the system described by Equations (13. 6) and (13. 7). The output vector Y(t) is)0()(X Cet Y At=And At e can be written as∑-==1)(n k kkAtA t e αHence, we obtain∑-==1)0()()(n t kkX CA t t Y αor)0()()0()()0()()(1110X CAt CAX t CX t t Y n n --+++=ααα (13. 8)If the system is completely observable, then, given the output Y(t) over a time interval ≤≤t t 0 1t , X(0)is uniquely determined from Equation (13. 8). It can be shown that this requires therank of the n nm ⨯matrix⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡-1n CA CA C to be n.From this analysis we can state the condition for complete observability as follows.The system described by Equation (13. 6) and (13. 7) is completely observable of and only is the n nm ⨯matrix⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡-1n CA CA C is of rank n or has n linearly independent column vectors. This matrix is called the observability matrix.Key Words and Terms1. controllability n. 可控性2. observability n. 可观测性3. controllable adj. 可控的4. observable adj. 可观测的5. mathematical model 数学模型6. property n. 性质，属性7. continuous-time system 连续时间系统 8. generality n. 一般性，普遍性 9. rank n. 秩10. linearly independent 线性无关 11. time-invariant system 时变系统 12. suffice v. 满足NotesAlthough most physic al systems are controllable an d observable, corresponding mathematical models may not possess the property of controllability and observability.尽管大多数的物理系统都是可控的和可观测的，它们所对应的数学模型并不一定具有可控性和可观测性。
BackStepping_Control
484Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.Chapter 20Back-Stepping Control of Quadrotor:A Dynamically Tuned Higher Order Like Neural Network ApproachAbhijit Das The University of Texas at Arlington, USAFrank Lewis The University of Texas at Arlington, USAKamesh Subbarao The University of Texas at Arlington, USA ABSTRACTThe dynamics of a quadrotor is a simplified form of helicopter dynamics that exhibit the same basic prob-lems of strong coupling, multi-input/multi-output design, and unknown nonlinearities. The Lagrangian model of a typical quadrotor that involves four inputs and six outputs results in an underactuated system. There are several design techniques are available for nonlinear control of mechanical underactuated system. One of the most popular among them is backstepping. Backstepping is a well known recursive procedure where underactuation characteristic of the system is resolved by defining ‘desired’ virtual control and virtual state variables. Virtual control variables is determined in each recursive step assum-ing the corresponding subsystem is Lyapunov stable and virtual states are typically the errors of actual and desired virtual control variables. The application of the backstepping even more interesting when a virtual control law is applied to a Lagrangian subsystem. The necessary information to select virtual control and state variables for these systems can be obtained through model identification methods. One of these methods includes Neural Network approximation to identify the unknown parameters of the system. The unknown parameters may include uncertain aerodynamic force and moment coefficients or unmodeled dynamics. These aerodynamic coefficients generally are the functions of higher order state polynomials. In this chapter we will discuss how we can implement linear in parameter first order neu -ral network approximation methods to identify these unknown higher order state polynomials in every recursive step of the backstepping. Thus the first order neural network eventually estimates the higher DOI: 10.4018/978-1-61520-711-4.ch020485Back-Stepping Control of Quadrotor1. INTRODUCTIONNowadays helicopters are designed to operate with greater agility and rapid maneuvering, and are capable of work in degraded environments including wind gusts etc. Helicopter control often requires holding at a particular trimmed state, generally hover, as well as making changes of velocity and acceleration in a desired way (T. J. Koo & Sastry). The control of unmanned rotorcraft is also becoming more and more important due to their usefulness in rescue, surveillance, inspection, mapping etc. For these applications the ability of the rotorcraft to maneuver sharply and hover precisely is important.Like fixed-wing aircraft control, rotorcraft control is also involved in controlling attitude pitch, yaw, and roll- and position, either separately or in a coupled way. But the main difference is that, due to the unique body structure of a rotorcraft, as well as the rotor dynamics, the attitude dynamics and position dynamics are strongly coupled. Therefore, it is very difficult to design a decoupled control law of good structure that stabilizes the faster and slower dynamics simultaneously. On the contrary, for a fixed wing aircraft it is easy to design decoupled standard control laws (B. L. Stevens & Lewis, 2003) with intui-tively comprehensible performance. Controllers of good structure are needed for robustness, as well as to give some intuitive feel for the functioning of autopilots, Stability Augmentation System (SAS), and Control Augmentation System (CAS).The dynamics of a quadrotor (A. Mokhtari, A. Benallegue, & Daachi, 2006; A. Mokhtari, A. Benal -legue, & Orlov, 2006; P. Castillo, R. Lozano, & Dzul, 2005a; S. Bouabdallah, A. Noth, & Siegwart, 2004; T. Madani & Benallegue, 2006) are a simplified form of rotorcraft dynamics that exhibit the basic problems including underactuation, strong coupling, multi-input/multi-output design, and unknown nonlinearities. In the quadrotor, the movement is characterized by the resultant forces and moments of four independent rotors. Control design for a quadrotor is quite similar to a rotorcraft; therefore the quadrotor serves as a suitable, more tractable, case study for rotorcraft controls design. In view of the similarities between a quadrotor and a rotorcraft, control design for the quadrotor reveals corresponding approaches for rotorcraft control design. The 6-DOF airframe dynamics of a typical quadrotor involves force and moment dynamics in which the position dynamics often appear as kinematics. Backstepping control is one of the solutions to handle such coupled dynamic-kinematic systems.There are many approaches such as (C. D. Yang & Liu, 2003; R. Enns & Si, 2000; R. Mahony & Hamel, 2005; V . Mistler, A. Benallegue, & M’Sirdi, 2001) etc. available which reveal different control techniques for rotorcraft models. Popular methods include input-output linearization and backstepping.order state polynomials which is in fact a higher order like neural net (HOLNN). Moreover, when these NN placed into a control loop, they become dynamic NN whose weights are tuned only. Due to the inherent characteristics of the quadrotor, the Lagrangian form for the position dynamics is bilinear in the controls, which is confronted using a bilinear inverse kinematics solution. The result is a control-ler of intuitively appealing structure having an outer kinematics loop for position control and an inner dynamics loop for attitude control. The stability of the control law is guaranteed by a Lyapunov proof. The control approach described in this chapter is robust since it explicitly deals with unmodeled state dependent disturbances without needing any prior knowledge of the same. A simulation study validates the results such as decoupling, tracking etc obtained in the paper.28 more pages are available in the full version of this document, which maybe purchased using the "Add to Cart" button on the product's webpage:/chapter/back-stepping-control-quadrotor/41679This title is available in InfoSci-Books, Business-Technology-Solution, InfoSci-Intelligent Technologies. 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