An algorithm for the optimal control of the driving of

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Look-ahead Control 62

Look-ahead Control 62

Hellström, Erik Look-ahead Control of Heavy Vehicles ISBN - - ISSN -
Cover illustration by E ektfabriken
AT X ε Typeset with L E Printed by LiU-Tryck, Linköping, Sweden
Linköping studies in science and technology Dissertations, No
Look-ahead Control of Heavy Vehicles
Erik Hellström
Department of Electrical Engineering Linköping
Байду номын сангаас
To Gabriella
A
Trucks are responsible for the major part of inland freight and so, they are a backbone of the modern economy but they are also a large consumer of energy. In this context, a dominating vehicle is a truck with heavy load on a long trip. e aim with look-ahead control is to reduce the energy consumption of heavy vehicles by utilizing information about future conditions focusing on the road topography ahead of the vehicle. e possible gains with look-ahead control are evaluated by performing experiments with a truck on highway. A real-time control system based on receding horizon control (RHC) is set up where the optimization problem is solved repeatedly on-line for a certain horizon ahead of the vehicle. e experimental results show that signi cant reductions of the fuel consumption are achieved, and that the controller structure, where the algorithm calculates set points fed to lower level controllers, has satisfactory robustness to perform well on-board in a real environment. Moreover, the controller behavior has the preferred property of being intuitive, and the behavior is perceived as comfortable and natural by participating drivers and passengers. A well-behaved and e cient algorithm is developed, based on dynamic programing, for the mixed-integer nonlinear minimum-fuel problem. A modeling framework is formulated where special attention is given to properly include gear shi ing with physical models. Fuel equivalents are used to reformulate the problem into a tractable form and to construct a residual cost enabling the use of a shorter horizon ahead of the vehicle. Analysis of errors due to discretization of the continuous dynamics and due to interpolation shows that an energy formulation is bene cial for reducing both error sources. e result is an algorithm giving accurate solutions with low computational e ort for use in an on-board controller for a fuel-optimal velocity pro le and gear selection. e prevailing approach for the look-ahead problem is RHC where main topics are the approximation of the residual cost and the choice of the horizon length. ese two topics are given a thorough investigation independent of the method of solving the optimal control problem in each time step. e basis for the fuel equivalents and the residual cost is formed from physical intuition as well as mathematical interpretations in terms of the Lagrange multipliers used in optimization theory. Measures for suboptimality are introduced that enables choosing horizon length with the appropriate compromise between fuel consumption and trip time. Control of a hybrid electric powertrain is put in the framework together with control of velocity and gear. For an e cient solution of the minimum-fuel problem in this case, more fuel equivalence factors and an energy formulation are employed. An application is demonstrated in a design study where it is shown how the optimal trade-o between size and capacity of the electrical system depends on road characteristics, and also that a modestly sized electrical system achieves most of the gain. e contributions develop algorithms, create associated design tools, and carry out experiments. Altogether, a feasible framework is achieved that pave the way for on-board fuel-optimal look-ahead control.

永磁同步电机最大转矩电流比控制

永磁同步电机最大转矩电流比控制

永磁同步电机最大转矩电流比控制一、本文概述Overview of this article随着能源危机和环境污染问题的日益严重,高效、环保的电机驱动系统成为了现代工业领域的研究热点。

永磁同步电机(PMSM)作为一种高性能的电机类型,因其高效率、高功率密度和良好的调速性能而被广泛应用于电动汽车、风力发电、机床设备等领域。

然而,为了充分发挥永磁同步电机的性能优势,有效的控制策略是至关重要的。

本文着重研究永磁同步电机的最大转矩电流比(MTPA)控制策略,旨在实现电机的高效、稳定运行。

With the increasing severity of energy crisis and environmental pollution, efficient and environmentally friendly motor drive systems have become a research hotspot in the modern industrial field. Permanent magnet synchronous motor (PMSM), as a high-performance motor type, is widely used in fields such as electric vehicles, wind power generation, and machine equipment due to its high efficiency, high power density, and good speed regulation performance. However, inorder to fully leverage the performance advantages of permanent magnet synchronous motors, effective control strategies are crucial. This article focuses on the maximum torque to current ratio (MTPA) control strategy of permanent magnet synchronous motors, aiming to achieve efficient and stable operation of the motor.最大转矩电流比控制是一种优化电机运行性能的控制方法,它通过调整电机的电流矢量,使得电机在相同电流幅值下产生最大的转矩输出。

(运筹学与控制论专业论文)线性规划的可行点算法

(运筹学与控制论专业论文)线性规划的可行点算法

摘要本文研究的是线性规划的可行点算法,一个由线性规划的内点算法衍生而来的算法.线性规划的内点算法是一个在线性规划的可行域内部迭代前进的算法.有各种各样的内点算法,但所有的内点算法都有一个共同点,就是在解的迭代改进过程中,要保持所有迭代点在可行域的内部,不能到达边界.当内点算法中的迭代点到达边界时,现行解至少有一个分量取零值.根据线性规划的灵敏度分析理论,对线性规划问题的现行解的某些分量做轻微的扰动不会改变线性规划问题的最优解.故我们可以用一个很小的正数赋值于现行锯中等于零的分量,继续计算,就可以解出线陛规划问题的最优解.这种对内点算法的迭代点到达边界情况的处理就得到了线性规划的可行点算法.它是一个在可行域的内部迭代前进求得线性规划的最优解的算法.在此算法中,只要迭代点保持为可行点.本文具体以仿射尺度算法和原始一对偶内点算法为研究对象,考虑这两种算法中迭代点到达边界的情况,得到相对应的’仿射尺度可行点算法’和’原始.对偶可行点算法,.在用理论证明线性规划的可行点算法的可行性的同时,我们还用数值实验验正了可行点算法在实际计算中的可行性和计算效果.关键词:线性规划,仿射尺度算法,原始一对偶内点算法,内点,可行点算法,步长可行点.AbstractderivedThisDaperfocusesonafeasiblepointalgorithmforlinearprogramming,analgorithmfromtheinteriorpointalgorithmsforlineza"programming.TheinteriorpointalgorithmsfindtheoptimalsolutionofthelinearprogrammingbysearchingwithinthefeasmleTe譬ionofthelinearprogramming.ThereareaUkindsofinteriorpointalgorithlrmalltheforlinearprogramnfing.Butalltheseinteriorpointalgorithmsshareaspeciality,whichissolution|terativeDointscannotreachtheboundsAccordingtothesensitivitytheory,theoptimalofthelinearprogrammingwillnotbechangedbylittledisturbancesofthepresentsolution·SoWeletthe{xjIzJ=o,J=1,2,-··)n)equalaverysmallpositivenunlber,goonwiththecomputatio“一andthenwegettheoptimalsolutionofthelinearprogramming.Alltheseleadtothedevelopment。

基于理想转向传动比的汽车线控转向控制算法

基于理想转向传动比的汽车线控转向控制算法

第37卷 第6期吉林大学学报(工学版)Vol.37 No.62007年11月Journal o f Jilin U niv ersity (Engineering and T echnolo gy Edition)Nov.2007收稿日期:2006 12 13.基金项目:国家自然科学基金资助项目(50475009); 863 国家高技术研究发展计划项目(2006AA 119192).作者简介:郑宏宇(1980 ),男,博士研究生.研究方向:汽车动态仿真与控制.E mail:zhy _jlu@163.co m 通讯联系人:宗长富(1962 ),男,教授,博士生导师.研究方向:汽车动态仿真与控制.E mail:zong.chang fu@基于理想转向传动比的汽车线控转向控制算法郑宏宇1,宗长富1,田承伟1,朱天军1,董义亮2,袁登木2(1.吉林大学汽车动态模拟国家重点实验室,长春130022; 2.长安股份有限公司汽车工程研究院,重庆401120)摘 要:以29自由度汽车动力学模型为基础,提出了保证汽车转向增益不变的理想传动比稳态控制策略,使线控转向汽车转向特性不受车速和方向盘转角变化的影响;提出了基于状态反馈的动态校正稳定性控制算法。

仿真和驾驶模拟器实验表明,基于理想转向传动比的稳态控制策略保证了汽车转向增益不变,减轻了驾驶员的负担,适合于更多的驾驶人群;基于状态反馈的动态校正稳定性控制算法有效提高了汽车的稳定性。

关键词:车辆工程;线控转向;转向传动比;稳态控制;稳定性控制中图分类号:U 463.4 文献标识码:A 文章编号:1671 5497(2007)06 1229 07Control algorithm for steer by wire system with ideal steering ratioZheng H ong yu 1,Zo ng Chang fu 1,Tian Cheng w ei 1,Zhu T ian jun 1,Do ng Yi liang 2,Yuan Deng m u 2(1.State K ey L abor atory ofA utomobile D y namics Simulation ,J ilin Univer sity ,Changchun 130022,China;2.I nstitute of A utomotiv e Engineer ing ,Chang an A utomobile H olding L td.,Chong qing 401120,China)Abstract:Based on a 29DOF v ehicle dynamic mo del,a steady state control strategy fo r the ideal steering ratio w as intr oduced to keep the vehicle steering g ain constant and make the steering character istic o f the steer by w ire sy stem not change w ith the vehicle speed and the steering angle;A stability control alg orithm w as proposed to correct the steering angle dynam ically based on the vehicle state feedback.The results of the simulatio n and the test in a driving simulator show ed that the introduced strateg y does keep the v ehicle steering g ain constant to reduce the driver bur den,allow ing the unskilled driver to steer the vehicle.The propo sed stability contro l algo rithm based on the vehicle state feedback improves effectively the vehicle stability.Key words:vehicle eng ineering;steering by wire;steer ratio;steady state control;stability control 目前汽车转向系统仍处于机械传动阶段,由于转向的角传动比固定,汽车转向特性随着车速和侧向加速度变化呈强非线性时变特性[1]。

优化深度确定性策略梯度算法

优化深度确定性策略梯度算法

2019,55(7)1引言深度强化学习(Deep Reinforcement Learning,DRL)近年大放异彩,Silver等人[1]研发的围棋程序AlphaGo成功打败世界顶级棋手李世石,在世界范围掀起了人工智能的热潮。

而AlphaGo的成功,归功于深度学习(Deep Learning,DL)和强化学习(Reinforcement Learning,RL)结合所产生的DRL的蓬勃发展。

2006年,Hinton等人[2]在《Science》上发表的文章首次提出了深度学习这一概念。

DL指训练多层神经网络,通过非线性变换,完成高维非线性的函数逼近或获取输入数据的高阶抽象特征。

由于近年来计算能力的飞速发展,DL取得了极大的进展,被广泛应用于计算机优化深度确定性策略梯度算法柯丰恺,周唯倜,赵大兴湖北工业大学机械工程学院,武汉430068摘要:深度强化学习善于解决控制的优化问题,连续动作的控制因为精度的要求,动作的数量随着动作维度的增加呈指数型增长,难以用离散的动作来表示。

基于Actor-Critic框架的深度确定性策略梯度(Deep Deterministic Policy Gradient,DDPG)算法虽然解决了连续动作控制问题,但是仍然存在采样方式缺乏科学理论指导、动作维度较高时的最优动作与非最优动作之间差距被忽视等问题。

针对上述问题,提出一种基于DDPG算法的优化采样及精确评价的改进算法,并成功应用于选择顺应性装配机器臂(Selective Compliance Assembly Robot Arm,SCARA)的仿真环境中,与原始的DDPG算法对比,取得了良好的效果,实现了SCARA机器人快速自动定位。

关键词:强化学习;深度学习;连续动作控制;机器臂文献标志码:A中图分类号:TP305doi:10.3778/j.issn.1002-8331.1712-0297柯丰恺,周唯倜,赵大兴.优化深度确定性策略梯度算法.计算机工程与应用,2019,55(7):151-156.KE Fengkai,ZHOU Weiti,ZHAO Daxing.Optimized deep deterministic policy gradient puter Engineering and Applications,2019,55(7):151-156.Optimized Deep Deterministic Policy Gradient AlgorithmKE Fengkai,ZHOU Weiti,ZHAO DaxingSchool of Mechanical Engineering,Hubei University of Technology,Wuhan430068,ChinaAbstract:Deep reinforcement learning is good at solving the optimization problems of control.Because of the accuracy requirements,with the increasing of action dimension,the number of action increases exponentially.So,it is difficult to express the continuous action with discrete action.The Deep Deterministic Policy Gradient(DDPG)algorithm,based on the Actor-Critic framework,solves the problem of continuous motion control.But there are still some problems,such as the lack of scientific theory of sampling,the neglect of the differences between optimal action and non-optimal action when the action dimension is relatively high.In order to solve these problems,this paper presents an improved algorithm with optimal sampling and precise critic for DDPG algorithm.And it is successfully applied to the simulation of Selective Compliance Assembly Robot Arm(SCARA).Compared with DDPG algorithm,an improvement effect is achieved and the SCARA robot is quickly and automatically positioned.Key words:reinforcement learning;deep learning;continuous action control;robot arm基金项目:国家自然科学基金(No.51675166)。

基于最优预瞄和模型预测的智能商用车路径跟踪控制

基于最优预瞄和模型预测的智能商用车路径跟踪控制

J Automotive Safety and Energy, Vol. 11 No. 4, 2020462—469基于最优预瞄和模型预测的智能商用车路径跟踪控制李耀华,刘 洋,冯乾隆,南友飞,何 杰,范吉康(长安大学汽车学院,西安710064,中国)摘要:为解决智能商用车路径跟踪问题,采用一种最优预瞄控制策略。

根据商用车航向角与路径曲率的关系,引入航向角偏差反馈控制;根据车速与预瞄距离的关系,提出了变权重因数的多点预瞄距离确定方法。

为了保证商用车路径跟踪的稳定性,采用模型预测控制策略,对车轮侧偏角进行约束。

通过TruckSim与Simulink联合仿真,对比分析了侧向偏差、横摆角速度和前轮侧偏角变化情况。

结果表明:最优预瞄控制策略对车速变化具有较好的适应性,但当路面附着因数较低时,车辆会失去稳定性;模型预测控制策略对车速和路面附着因数变化都具有较好的适应性,行驶稳定性更好,且比最优预瞄控制策略具有更精确的路径跟踪效果。

关键词:智能商用车;路径跟踪;路面附着因数;最优预瞄控制;模型预测控制中图分类号: U 471.15 文献标识码: A DOI: 10.3969/j.issn.1674-8484.2020.04.005Path tracking control for an intelligent commercial vehicle based on optimal preview and model predictiveLI Yaohua, LIU Yang, FENG Qianlong, NAN Youfei, HE Jie, FAN Jikang(School of Automobile, Chang’an University, Xi’an 710064, China)Abstract: An optimal preview control strategy was adopted to solved path tracking problem of intelligent commercial vehicles. According to the relationship between the heading angle and the curvature of the path,the heading angle deviation feedback control was introduced. According to the relationship between the speedand the preview distance, a multi-point preview distance determination method with variable weight coefficientwas proposed. In order to ensure the stability of path tracking, the model predictive control was used to restrictthe wheel sideslip angle. Through co-simulation of TruckSim and Simulink, the lateral deviations, the yaw ratesand the front wheel slip angles were compared. The results show that the optimal preview control has good adaptability to the speed, but when the road adhesion factor is low, the vehicle will lose stability; The model predictive control has better adaptability to speeds and road adhesion factors, and has better driving stability,and has more accurate path tracking effect than the optimal preview control.Key words:i ntelligent commercial vehicles; path tracking; road adhesion factors; optimal preview control;model predictive control收稿日期 / Received :2020-07-18。

并网逆变器数字化控制算法优化设计

并网逆变器数字化控制算法优化设计

电气传动2022年第52卷第1期摘要:数字控制器存在的固有控制时延会影响并网逆变器入网电流的控制性能,为此提出一种控制算法优化方案,旨在降低控制时延带来的不利影响。

首先分析了控制时延以及零阶保持器对比例-积分(propor⁃tional-integral ,PI )控制器参数稳定域及系统阶跃响应的影响,在此基础上提出采用超前环节来补偿控制时延带来的相角滞后,分析不同补偿参数下的性能差异并选定了最优补偿参数,经过超前环节补偿后的PI 控制算法能拓宽PI 参数的稳定域以及提升控制系统动态性能。

最后,利用Matlab/Simulink 仿真平台以及并网逆变器样机验证了所提算法的有效性与实用性。

关键词:并网逆变器;控制时延;比例积分控制;超前补偿;稳定域中图分类号:TM464文献标识码:ADOI :10.19457/j.1001-2095.dqcd21978Optimization Design of Digital Control Algorithm for Grid Connected InverterTAN Lingqi 1,SUN Xiaomin 2,LI Xinwei 1,HUANG Yangjue 1,ZHAO Wei 1(1.Guangdong Key Laboratory of Electric Power Equipment Reliability (Electric Power ResearchInstitute of Guangdong Power Grid Co.,Ltd.),Guangzhou 510080,Guangdong ,China ;2.Guangdong Power Grid Co.,Ltd.,Guangzhou ,Guangdong 510060,China )Abstract:The inherent control delay of the digital controller may affect the control performance of the current of grid-connected inverter.For this reason ,a control algorithm optimization scheme was proposed to reduce the adverse effect of control delay.Firstly ,the influences of control delay and zero-order hold on the parameters stability region of proportional -integral (PI )controller and step response of system were analyzed.On this basis ,a leading link based compensator was proposed to compensate the phase lag caused by control delay ,whilst the performance difference under various compensation parameters was also analyzed.Therefore the optimal compensation parameter was selected and determined.And the PI control algorithm with compensator based on leading link could broaden the stability region of PI parameters and improve the dynamic performance of control system.Finally ,the validity and effectiveness of the proposed algorithm were verified via the Matlab/Simulink simulation platform and a grid-connected inverter prototype.Key words:grid-connected inverter ;control delay ;proportional-integral (PI )controller ;lead compensation ;region of stability基金项目:中国南方电网有限责任公司科技项目(GDKJXM20180311)作者简介:谭令其(1991—),男,硕士,工程师,Email :******************并网逆变器数字化控制算法优化设计谭令其1,孙晓敏2,李歆蔚1,黄杨珏1,赵伟1(1.广东省电力装备可靠性企业重点实验室(广东电网有限责任公司电力科学研究院),广东广州510080;2.广东电网有限责任公司,广东广州510060)并网逆变器是新能源发电系统与交流电网的接口,并网电流控制算法直接关系到并网逆变器的性能。

温室大棚内环境监测系统硬件设计外文文献

温室大棚内环境监测系统硬件设计外文文献

外文翻译毕业设计题目:温室大棚环境监测系统硬件设计原文:Environmental Monitoring and GreenhouseControl by DistributedSensor Network译文:环境监测与温室分布式传感器网络控制Environmental Monitoring and Green house Control by DistributedSensor Network(原文)A sensor is a miniature component which measure physical parameters from the environment. Sensors measure the physical parameters and transmit them either by wired or wireless medium. In wireless medium the sensor and its associated components are called as node. A node is self-possessed by a processor, local memory, sensors, radio, battery and a base station responsible for receiving and processing data collected by the nodes. They carry out joint activities due to limited resources such as battery, processor and memory. Nowadays, the applications of these networks are numerous, varied and the applications in agriculture are still budding. One interesting application is in environmental monitoring and green house control, where the crop conditions such as climate and soil do not depend on natural agents. To control and monitor the environmental factors, sensors and actuators are necessary. Under these circumstances, these devices must be used to make a distributed measure, spreading sensors all over the greenhouse using distributed clustering. This paper reveals an idea of environmental monitoring and greenhouse control using a sensor network. The hardware implementation shows periodic monitoring and control of greenhouse gases in an enhanced manner. Future work is concentrated in application of the same mechanism using wireless sensor network.Keywords—Sensor, sensor nodes, wireless sensor network (WSN), greenhouse control, environmental monitoring, CO 2monitoring, distributed clustering.I. INTRODUCTIONA sensor is able to convert physical or chemical readings gathered from the environment into signals that can be calculated by a system. A multi sensor node is able to sense several magnitudes in the same device. In a multi sensor, the input variables might be temperature (it is also able to capture nippy changes of temperature), fire, infrared radiation, humidity, smoke and CO2. A wireless sensor network could be an useful architecture for the deployment of the sensors used for fire detection and verification. The most vital factors for the quality and productivity of plant growth are temperature,humidity, light and the level of the carbon dioxide. Constant monitoring of these environmental variables gives information to the farmer to better understand, how each factor affects growth and how to manage maximal crop productiveness.The optimal greenhouse [3] climate adjustment can facilitate us to advance productivity and to achieve remarkable energy saving, particularly during the winter in northern countries. In the past generation greenhouses it was enough to have one cabled measurement point in the middle to offer the information to the greenhouse automation system. The system itself was typically simple without opportunities to manage locally heating, lights, ventilation or some other activity, which was affecting the greenhouse interior climate. The typical size of the greenhouse itself is much larger than it was before, and the greenhouse facilities provide several options to make local adjustments to the light, ventilation and other greenhouse support systems. However, additional measurement data is also needed to construct this kind of automation system to work properly. Increased number of measurement points must not dramatically augment the automation system cost. It should also be possible to easily alter the location of the measurement points according to the particular needs, which depend on the specific plant, on the possible changes in the external weather or greenhouse structure and on the plant placement in the greenhouse. Wireless sensor network can form a useful part of the automation system architecture in modern greenhouses constructively. Wireless communication can be used to collect the measurements and to communicate between the centralized control and the actuators located to the different parts of the greenhouse. In advanced WSN solutions, some parts of the control system itself can also be implemented in a distributed manner to the network such that local control loops can be formed. Compared to the cabled systems, the installation of WSN is fast, cheap and easy. Moreover, it is easy to relocate the measurement points when needed by just moving sensor nodes from one location to another within a communication range of the coordinator device. If the greenhouse vegetation is high and dense, the small and light weight nodes can even be hanged up to the plants’ branches. WSN maintenance is also relatively cheap and easy. The only additional costs occur when the sensor nodes run out of batteries (figure 1) and the batteries need to be charged or replaced, but the lifespan of the battery can be several years if an efficient power saving algorithm is applied. In this work, the very first steps towards the wireless greenhouse automation system by building a wireless measuring system for that purpose is taken and by testing its feasibility and reliability with a simple experimental setup.Clustering [11, 12] may be centralized ordistributed, based on the arrangement ofCH. In centralized clustering, the CH ispreset but in distributed clustering CH hasno fixed architecture. Distributedclustering mechanism is used for someprivate reasons like sensor nodes prone to Figure 1:Various components of a sensor nodefailure, better collection of data and minimizing redundant information. Hence these distributed clustering mechanisms encompass highly self-organizing capability.II. RELATED WORKS IN SENSOR NETWORKMilitary applications are very closely linked to the awareness of wireless sensor networks. In fact, it is very harsh to say for sure whether motes were developed because of military and air defense needs or whether they were invented separately and were subsequently applied to army services. Regarding military applications, the region of concentration extents from information collection, generally, to enemy tracking or battlefield surveillance. For example, mines could be regarded as unsafe and obsolete in the future and may be replaced by thousands of isolated sensors that will detect an intrusion of unreceptive units.Outdoor monitoring is an additional celestial area for applications of sensors networks. One of the most delegate examples is the operation of sensor nodes on Great Duck Island [8]. This sensor network has been used for environment monitoring. The sensor nodes used were talented to sense temperature, barometric pressure and humidity [1, 2]. In addition, passive infrared sensors and photo resistors were betrothed. The array was to monitor the natural environment of a bird and its activities according to climatic changes. For that cause, several motes were installed within birds’ burrows, to spot out the bird’s presence, while the rest were deployed in the nearby areas. Data are aggregated by the employment of sensor nodes and are passed through to a gateway.Management of costly possessions like equipment, machinery, different types of stock or products can be a quandary. The dilemma is highly distributed, as these companies enlarge all over the world. A gifted method to achieve asset tracking and cope with this trouble is believed to be with the use ofsensor networks. The application of wireless sensors in petroleum bunks and chemical warehouses refers to warehouses and cargo space administration of barrels. The thought is that motes attached to barrels will be gifted to locate nearby objects (other barrels), detecting their content and alerting in case of inappropriateness with their own, aging effects of the field etc.Health science and the health care system can also yield from the employment of wireless sensors. Applications in this class include telemonitoring human physiological data remotely, tracking and monitoring of doctors and patients within a hospital, drug superintendent in hospitals, etc. In Smart Sensors, retina prosthesis chip consisting of 100 micro sensors are built within the human eye. This allows patients with inadequate vision to see at an adequate level. Cognitive disorders, which almost certainly direct to Alzheimer’s, can be monitored and controlled at their premature stages with these wireless sensors.Robotic applications [9, 10] previously implemented are the unearthing of level sets of scalar fields using mobile sensor networks and imitation of the function of bacteria for looking for and discovering dissipative gradient sources. The tracking of a light source is completed with a few of the easy algorithms. In addition, a reply to the coverage crisis by robots and motes is accomplished for thick measurements over a broad area. The connection of both static and mobile networks is accomplished with the help of mobile robots, which travel around the environment and set up motes that act as beacons. The beacons support the robots to portray the directions. The mobile robots can perform as gateways into wireless sensor networks. Examples of such tasks are: sustaining energy resources of the wireless sensor network indefinitely, maintaining and configuring hardware, detecting sensor failure and appropriate deployment for connectivity amid the sensor nodes.Landslide detection employs sprinkled sensor system for predicting the happening of the landslides. The consideration of predicting landslides by means of sensor networks arose out of a must to mitigate the blemish caused by landslides to human lives and to the railway networks. A mixture of techniques from earth sciences, signal processing, distributed systems and fault-tolerance is used. One solitary trait of these systems is that it combines several distributed systems techniques to deal with the complexities of a distributed sensor network environment where connectivity is underprivileged and power budgets are very constrained, while fulfilling real-world requirements of safety. Generally these methods use a set of inexpensive single-axis strain gauges attached to cheap nodes, each with a CPU, battery and elite wireless transmitter block.Forest fires, also recognized as wild fires are wild fires occurring in wild areas and root major damage to natural and human resources. Forest fires wipes out forests, blaze the infrastructure and might result in high human death toll closer to urban areas. Common causes of forest fires embrace lightning, human carelessness and revelation of fuel to extreme heat and aridity. It is well known that in few cases fires are constituent of the forest ecosystem and they are important to the life cycle of native habitats.Sensor-Clouds can be used for health monitoring by using a quantity of simply obtainable and most often wearable sensors like accelerometer sensors, proximity and temperature sensors and so forth to collect patient’s health-related statistics for tracking sleep activity pattern body temperature and other respiratory conditions. These wearable sensor devices must have sustain of Bluetooth’s wireless interface, Ultra wideband and so forth interface for streaming of data, linked wirelessly to any smart phone through the interface. These smart phone devices foresee performing like a gateway between the remote server and sensor through the internet.III. EXPERIMENTAL SETUP IN A GREENHOUSEA. The Greenhouse EnvironmentA modern greenhouse [4-6] can consist of plentiful parts which contain their own local climate variable settings. As a result, a number of measurement points are also needed. This class of environment is challenging both for the sensor node electronics and for the short-range IEEE 802.15.4wireless network, in which communication range is greatly longer in open environments.B. SensorsHasty response time, low power consumption and tolerance against moisture climate, relative humidity and temperature sensor forms a perfect preference and solution for the greenhouse environment. Communication amid sensor and node can be carried out by IIC interface. Luminosity can be measured by light sensor, which converts light intensity to voltage. Unstable output signal is handled by low-pass filter to get correct luminosity values. CO2 measuring [7] takes longer time than other measurements and CO2 sensor voltage supply have to be within few volts. The carbon dioxide value can be read from the ensuing output voltage. Operational amplifier raises the voltage level of otherwise frail signal from the sensor.C. GreenhousesA greenhouse is a configuration covering ground frequently used for growth and progress of plants that will return the owner’s risk time and capital. This display is mounted with the purpose of protecting crop and of allowing a better environment to its progress. This shield is enough to promise a superior quality in production in some cases. However, when the major purpose is to achieve a better control on the horticulture development, it is necessary to test and control the variables that influence the development of a culture. The chief function of a greenhouse is to provide a more sympathetic environment than outside. Unlike what happens in traditional agriculture, where crop conditions and yield depend on nature resources such as climate, soil and others, a greenhouse ought to guarantee production independently of climatic factors. It is noteworthy to observe that even though a greenhouse protects crop from exterior factors such as winds, water excess and warmth it may cause plentiful problems such as fungus and excessive humidity. Therefore, mechanisms to scrutinize and control a greenhouse environment are incredibly vital to achieve better productivity. To get superior productivity and quality, better control system is necessary and as a result the production costs also get reduced. The chief elements involved in a greenhouse control system are: temperature, humidity, CO 2 concentration, radiation, water and nutrients.D. TemperatureTemperature is one of the most key factors to be monitored because it is unswervingly related to the growth and progress of the plants. For all plants, there is a temperature range considered best and to most plants this range is relatively varying between 10ºC and 30ºC. Among these parameters of temperature: extreme temperatures, maximum temperature, minimum temperature, day temperature and night temperature, difference between day and night temperatures are to be vigilantly considered.E. Water and HumidityAnother momentous factor in greenhouses is water. The absorption of water by plants is linked to the radiation. The lack or low level of water affects growth and photosynthesis. Besides air, the ground humidity also adjust the development of plants. The air humidity is interrelated to the transpiration while the ground humidity is connected to water absorption and photosynthesis. An atmosphere with extreme humidity decreases plants transpiration, reducing growth and may promote the proliferation of fungus. On the other hand, squat humidity level environments might cause dehydration.F. RadiationRadiation is a fundamental element in greenhouse production and sunlight is the key source of radiation. It is an important component for photosynthesis and carbon fixing. The significant radiation features are intensity and duration. The radiation intensity is linked to plant growth and the duration is openly associated with its metabolism.G. CO2 ConcentrationCO2 is an essential nutrient for plant development, allowing the assimilation of carbon. The carbon retaining procedure occurs through the photosynthesis when plants take away CO2 from the atmosphere. During the photosynthesis, the plant uses carbon and radiation to produce carbohydrate, whose function is to permit the plant development. Therefore, an enriched air environment should contribute to plant growth, but it is also vital to note that an extreme carbon level may turn the environment poisonous.IV. THE PROPOSED MODELA solution to the existing drawbacks can be found out from this proposed model. The proposed model is implemented in hardware, tested and the results show an excellent improvement in the sensing parameters when compared to the existing set of environmental monitoring and greenhouse control models. Sensor arrays like temperature sensor, light sensor, humidity sensor and vibration sensors are incorporated in the board. The sensed data is processed by the micro controller and displayed in the LCD display. Wireless transmission of the parameters is accomplished by a ZigBee module that sends information to the remote monitoring station periodically. To control and monitor the environmental variables planned in an earlier section, sensors and actuators capable of measuring and controlling the values inside the greenhouse are necessary. Generally, a greenhouse control is implemented just by approximating a measured cost to a reference or ideal cost. Figure 2, shows the basic block diagram of the proposed model. Due to cost considerations, the proposed model uses sensor network instead of wireless sensor network. The sensed data is forwarded to the gateway. The gateway then forwards the data to the remote monitoring base station. The base station is a remotely located software configured computer, where the monitored details are periodically visualized to carry out further control actions.Figure 2: Block diagram of the proposed modelIn the proposed model, the ideal assessment depends on the culture and type of plant. Control systems can be separated into centralized and distributed systems. In a centralized system a single constituent is responsible for gathering and processing the data. So, all the components of the system are connected to this solitary element. In a distributed control system the connections between nodes and information processing is distributed amongst the system components. The focal advantages of a distributed system may include: Reliability: a component failure affects barely part of the structure, Expansion: the likelihood of adding up of a new component without enormous changes in the system, Flexibility: changes in the procedure such as adding, removing and substituting of components impacts merely in the components involved in these basic operations. The major trouble of these technologies is that they are not developed for WSN and they do not present mechanisms to improve energy consumption.In this way, it is probable to check all places inside the greenhouse, identifying not only local values as in many applications, but checking real world and distributed values. Therefore, the greenhouse control ought to be improved, allowing a settlement in a way that the complete environment can be adjusted as close as feasible to a set point. It is essential to observe that, in most applications the sensors are placed in a point of a greenhouse and the measures gained are used to direct the entire greenhouse. However, even though in a controlled and relatively tiny place like a greenhouse, it is possible to have different values of climatic agents. Figure 3 shows the experimental setup forenvironmental monitoring.Figure 3: Experimental setup for environmental monitoringThus, the use of sensor in a greenhouse environment should permit a real time monitoring and an improved measurement through convenient distribution. The collected data in the system proposed must be sent to a base station located outside the greenhouse. The base station is connected by a gateway. With the implementation of this architecture, each node will be answerable for data collecting through its sensors and for sending it to its neighbors until all collected data emerge at the base station. The gateway generally uses wireless and Ethernet communication. The base station will be accountable for managing collected data, so some greenhouse control soft wares and some wireless actuators are necessary. In this application node defense will also be necessary to avoid damage by water and inputs. It is imperative to emphasize that the use of wireless sensors and actuators is advantageous to make the system installation trouble-free and to obtain flexibility and mobility in the nodes prototype. The difficulties in applying WSN in agricultural applications might include costs and lack of standardization on WSN communication protocols. Due to cost constraints, the proposed model is designed with sensors. In future, the same sensor network will be simulated in NS-2 for a distributed clustering mechanism. Wireless sensor network with temperature, moisture and light sensing and advanced capabilities will be implemented in real-time environment for green house monitoring in future.V. DISCUSSIONSThe major contributions of this manuscript are as follows. The design and implementation oflarge-scale and long-term CO2 monitoring sensor network is discussed. A low-cost sensor deployment strategy with guaranteed performance which addresses the sensor deployment problems in the existing models has been proposed. Hardware implementation of this model has been done and the parameters are periodically monitored with few sensors.VI. CONCLUSION AND FUTURE WORKA model of agricultural application using sensor networks for greenhouses monitoring and control was presented. The wireless sensor network technology, although under development, seems to be promising mainly because it allows real time data acquisition. However, for such agricultural application to be developed, some technological challenges should be resolved. A greenhouse is a controlled environment and does not require a lot of climatic parameters to be controlled. The use of this technology in large scale seems to be something for the near future. In this application, the great number of climatic parameters can be monitored using the sensors available. As a greenhouse is a relatively small and controlled environment, and energy is a limited resource, the possibility of replacing batteries or even resorting to a steady energy source adaptation is a constructive aspect. This paper reveals an idea of environmental monitoring and greenhouse control using a sensor network. The hardware implementation shows periodic monitoring and control of greenhouse gases in an enhanced manner. Future work is concentrated in application of the same mechanism using wireless sensor network. This technology can also be applied in breeding of confined animals in precision zoo, where the sensor nodes should send information about animal temperature, pressure and other vital signals to guarantee a healthy environment to animals. In order to attain better energy efficiency, this mechanism will be implemented in real-world wireless sensor network, with a well-known energy efficient distributed clustering mechanism (HEED).Author:CoimbatoreNationality:IndiaSource:Int. J. Advanced Networking and Applications V olume: 5 Issue: 5 Pages:2060-2065分布式传感器网络环境监测与温室控制(译文)传感器是一种微型组件可测量环境中的物理参数。

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have been proposed to obtain the switching time points and/or the cruise speed, which are the remaining decision variables, see e.g. [6].Reported practical implementations are often limited to obtaining the time point when coasting should start, e.g. [7], or to additionally calculate the optimal cruise speed, e.g. [8]. However, the analytical solution to the simplified linear problem has a number of limitations. Some authors propose empirical improvements, e.g. to replace the section of constant cruise speed with a coast remotor cycle [8], or t o exploit the height profile of the track [SI. Moreover, the number of decision variables increases if the maximal permitted track speed varies during a ride. The present paper describes a new algorithm for the optimal train control problem. We consider a nonlinear train model that is a closer approximation of reality. This allows us to overcome the limitations of the more simple linear solution, accounting for effects such as air resistance, which cannot be neglected at higher speeds. Most importantly, the new approach takes into account the setpoint-dependent efficiency of the propulsion system, resulting in a solution that is both qualitatively and quantitatively different from the solution to the simplified problem. Using kinetic energy instead of velocity as a dynamic state variable, an analytical solution for the nonlinear equations of motion can be found. Exploiting the numerical efficiency of this analytical solution instead of numerical integration of the nonlinear model, we implement a discrete dynamic programming algorithm that is able to solve the optimal control problem in real time.
0-7803-663&7/00$10.00 0 2000 lEEE
2 Train model
The train is commonly modeled as mass point in the related literature, see e.g. [6]. Note that this is not a limitation compared to a distributed mass model, as the distribution of masses over the length of the train can be taken into account by pre-processing the altitude profile of the track before it is used with the point-mass model. Starting from this modeling assumption, we decide t o use kinetic energy per mass unit ekin = 0 . 5 ~ ~ time and t as states, and position s of the train as the independent variable. The use of position s instead of time
1 Introduction The optimal control of trains has been an active research topic for many years. The general aim is to drive a train in a way that minimizes overall consumption of electrical energy, subject t o constraints on time ‘and to physical limitations imposed by the train and the operating environment. In an increasingly competitive transportation market, interest in energy efficiency among railway operators has been a subject of increased interest in recent years, both for retrofit of existing vehicles and the aquisition of new ones. Literature on energy-efficient operation of trains goes back to the late 1960s, when the optimal control problem for a simplified linear train model was solved analytically by applying Pontryagin’s Maximum principle, see e.g. [2]. The resulting optimal driving style basically consists of four sections: maximum acceleration, cruising at constant speed, coasting (zero traction force), and maximum deceleration. Several methods
Rudiger Franke ABB Corporate Research

Peter Terwiesch ABB Corporate Researcranz (Switzerland)
2123
t as independent variable simplifies the incorporation of track-related data, such as track slope, speed limits, and tunnel resistance. Depending on the exact local conditions, information on s is obtained in real time by a combination of global positioning signals, trackside equipment, gyro, and model integration, making it known to an accuracy of about 10 meters. Digital track maps with information on gradient, altitude, speed limits, tunnels, etc are available in a number of countries with a resolution of 0.1 to 1meter. Note that the choice of ekin instead of speed w does not eliminate all model nonlinearities. However, not only does the influence of air resistance on the states become linear, but also the nonlinearity is such that a piecewise analytical solution of the model differential equations becomes possible, thus permitting a significant increase in the speed of numerical solutions. Our equations of motion have the form
Proceedingsof the 39” lEEE Conference on Decision and Control Sydney, Australia December, 2000
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