关于fuzzy logic的简述(英文)

合集下载

模糊理论综述

模糊理论综述引言模糊理论（Fuzzy Logic)是在美国加州大学伯克利分校电气工程系的L.A.zadeh（扎德）教授于1965年创立的模糊集合理论的数学基础上发展起来的,主要包括模糊集合理论、模糊逻辑、模糊推理和模糊控制等方面的内容.L.A.Zadeh教授在1965年发表了著名的论文，文中首次提出表达事物模糊性的重要概念：隶属函数，从而突破了19世纪末康托尔的经典集合理论,奠定模糊理论的基础。

1974年英国的E.H.Mamdani成功地将模糊控制应用于锅炉和蒸汽机的控制，标志着模糊控制技术的诞生。

随之几十年的发展，至今为止模糊理论已经非常成熟，主要包括模糊集合理论、模糊逻辑、模糊推理和模糊控制等方面的内容。

模糊理论是以模糊集合为基础，其基本精神是接受模糊性现象存在的事实，而以处理概念模糊不确定的事物为其研究目标，并积极的将其严密的量化成计算机可以处理的讯息，不主张用繁杂的数学分析即模型来解决问题。

二、模糊理论的一般原理由于客观世界广泛存在的非定量化的特点，如拔地而起的大树，人们可以估计它很重，但无法测准它实际重量。

又如一群人，男性女性是可明确划分的，但是谁是“老年人”谁又算“中年人”；谁个子高，谁不高都只能凭一时印象去论说，而实际人们对这些事物本身的判断是带有模糊性的，也就是非定量化特征。

因此事物的模糊性往往是人类推理，认识客观世界时存在的现象。

虽然利用数学手段甚至精确到小数点后几位，实际仍然是近似的。

特别是对某一个即将运行的系统进行分析，设计时，系统越复杂，它的精确化能力越难以提高。

当复杂性和精确化需求达到一定阈值时，这二者必将出现不相容性，这就是著名的“系统不相容原理”。

由于系统影响因素众多，甚至某些因素限于人们认识方法，水准，角度不同而认识不足，原希望繁荣兴旺，最后导致失败，这些都是客观存在的。

这些事物的现象，正反映了我们认识它们时存在模糊性。

所以一味追求精确，倒可能是模糊的，而适当模糊以达到一定的精确倒是科学的，这就是模糊理论的一般原理。

模糊算法的简介与应用领域

模糊算法的简介与应用领域模糊算法（Fuzzy Logic）是一种基于逻辑的数学方法，可用于计算机和控制工程中的问题。

Fuzzy Logic是指用于处理不确定性或模糊性问题的逻辑工具。

通过将问题的变量转换为可量化的值，并对变量进行分层，以确定如何进行推理，并进行决策。

模糊逻辑的核心是将不确定性转化为数字，然后使用公式进行操作，以确定结果。

例如，考虑一个简单的问题：如果一个人有160cm，那么这个人是否矮？根据模糊逻辑，这个问题不能被简单地回答“是”或“否”。

相反，问题需要考虑到不同的因素，例如人口统计数据，文化背景和其他因素，以确定是否可以说这个人是矮的。

模糊逻辑可以应用于各种各样的领域，包括工程控制，人工智能，自然语言处理，机器人技术等。

在这些领域中，模糊逻辑被用来处理复杂的系统和问题，并为决策提供精确而可靠的方法。

在工程控制中，模糊逻辑被广泛用于计算机和机器人系统的设计和开发。

例如，在机器人技术领域，模糊逻辑被用来控制机器人的运动和行为，以便机器人能够正确地执行任务。

此外，模糊逻辑也被用于控制汽车，飞机和其他机械设备等的操作。

在人工智能领域，模糊逻辑被用于自然语言处理和模式识别。

模糊逻辑可以帮助计算机系统理解模糊或不确定的语言和概念，并在模式识别方面提供更精确的方法。

在这个领域，模糊逻辑还被用于计算机视觉和图像处理。

在现代社会中，模糊逻辑广泛应用于人们的日常生活中。

例如，在车辆安全系统中，模糊逻辑用于判断车辆的速度和距离，以确定何时应该自动刹车。

此外，在消费电子产品中，模糊逻辑被用于改进电视机和音响系统等的品质。

总之，模糊逻辑是一种强大的工具，可以用于各种领域的问题和应用。

模糊逻辑不仅提供了一种新的方法来处理和解决问题，而且为我们提供了更精确的工具来做出决策。

英文单片机模糊逻辑控制器对永磁直流电动机的设计和应用

Engineering Applications of Artiﬁcial Intelligence 18 (2005) 881–890Single-chip fuzzy logic controller design and an application on a permanent magnet dc motorSinan PravadaliogluI.M.Y.O., Control Sys. Department, Dokuz Eylu ¨l University, Menderes cad, Istasyon sok 5, Buca, 35170 Izmir, Turkey Received 27 March 2004; accepted 11 March 2005 Available online 23 May2005AbstractThis paper describes a low-cost single-chip PI-type fuzzy logic controller design and an application on a permanent magnet dc motor drive. The presented controller application calculates the duty cycle of the PWM chopper drive and can be used to dc–dcconverters as well. The self-tuning capability makes the controller robust and all the tasks are carried out by a single chip reducing the cost of the system and so program code optimization is achieved. A simple, but effective algorithm is developed to calculate numerical values instead of linguistic rules. In this way, external memory usage is eliminated. The contribution of this paper is to present the feasibilityof a high-performance non-linear fuzzy logic controller which can be implemented byusing a general purpose microcontroller without modiﬁed fuzzy methods. The developed fuzzy logic controller was simulated in MATLAB/SIMULINK. The theoretical and experimental results indicate that the implemented fuzzy logic controller has a high performance for real-time control over a wide range of operating conditions.2005 Elsevier Ltd. All rights reserved.Keywords: Dc motor drive; Fuzzy logic controller; Microcontroller; Application; Simulation1.IntroductionIn switch-mode power supplies, the transformation of dc voltage from one level to another level is dc–dc conversion and accomplished by using dc–dc converter circuits, which offers higher efficiency than linear regulators. They have great importance in many practical electronic systems, including home appliances, computers and communication equipment. They are also widely used in industry, especially in switch-mode dc power supplies and in dc motor drive applications. The dc- dc converter accepts an unregulated dc input voltage and produces a controlled dc output at desired voltage level. They can step-up, step-down and invert the input dc voltage and transfer energy from input to output in discrete packets. The one disadvantage of dc–dc converters is noise. At every period to charge in discrete packets, it creates noise or ripple. The noise can be minimized using specific control techniques and with convenient component selection. There are well-knowncontrol techniques including pulse-width modulation (PWM) where the switch frequencyis constant and the duty cycle varies with the load.PWM technique affords high efficiency over a wide load range. In addition, because the switching frequency is fixed, the noise spectrum is relatively narrow, allowing simple low-pass filter techniques to reduce the peak-to- peak voltage ripple. For this same reason, PWM is popularwith telecom power supply applications where noise interference is of concern .The most important requirement of a control system for the dc–dc converter is to maintain the output voltage constant irrespective of variations in the dc input voltage and the load current. However, load changes affect the output transiently and cause significant deviations from the steady-state level of dc output voltage, which must be controlled to equal adesired level by the control systems. The inherent switching of a dc–dc converter results in the circuit components being connected periodically changing configurations, each configuration being described by a set of separate equations. Transient analysis and control system design for a converter is therefore difficult since a number of equations must be solved in sequence. Although the state-space averaging is the most commonly used model to obtain linear transfer functions to solve this problem, it neglects significant parts of non-linear behavior of dc–dc converters. Development of non-linear controllers for dc–dc converters have gained considerable attention in recent years.A fuzzy logic model based controller is chosen as the non-linear controller for this study. Fuzzy logic control (FLC) has been an important research topic. Despite the lack of concrete theoretical basis many successful applications on FLC were reported and various applications for dc–dc converters and electrical drives have been published and can be found in the literature (So et al., 1996, 1995;Mattavelli et al., 1997; Brandsetter and Sedlak, 1996; Hyo et al., 2001; Gupta et al., 1997;Zakharov, 1996; V as, 1998, 1999). FLC has a wide-spread application on the non-linear and complex systems as well as linear systems due to its capability to control the systems that might not have a transfer function between input and output variables. Experi-ences show that fuzzy control can yield superior results to those obtained by conventional control algorithms.In the meantime, new fuzzy microcontroller chips are available on the market and are able to execute fuzzy rules very fast with their mask programmed algorithms that have some drawbacks such as restriction in implementing any desired algorithm. Digital signal processing (DSP) integrated circuits (IC) are capable of computing and processing the system variables very quickly with high precision. But most of the DSP circuits are expensive and do not contain peripherals such as analog to digital (A/D) and digital to analog (D/A) circuits for conversion and PWM generator on chip,and need to be added externally. A fuzzy controller application among the others on dc–dc converters used a TMS320-DSP and fuzzy controller with an evaluation module plus some external chips. They were an A/D converter for feedback signal evaluation, a D/A converter for converting the calculated quantity into a control output and a PWM chip to generate the appropriate duty cycle for the semiconductor switching elements (So et al., 1995; Brandsetter and Sedlak, 1996). An implementation of an FLC with 8-bit conventional microcontroller is presented in Gupta et al. (1997) for dc–dc converters and a modified centroid method for defuzzication process is used to reduce the processing time of 8-bit microcontroller. However, this moded defuzzification technique increases the settling time of the system. A detailed simulation and experimental study on the closed-loop control of dc motor drive with FLC is carried out in Zakharov (1996) and a PC computer with an evaluation board is employed for FLC. The transient response of proportional integral (PI)-type current and speed controllers are compared to that of the FLC.The aim of the study presented in this paper is to design and to implement a high-performance fuzzy tuned PI controller for controlling the rotor speed of permanent magnet dc motor (PMDCM). We also provide a wayof designing such a controller in a cost effective way by using a general purpose single-chip microcontroller. This design is implemented with-outmaking the assumptions for the modification of defuzzification process which was presented in Guptaet al. (1997). This leads to an improved performance of the transient and steadystate of the closed-loop System.The experimental results are also compared to the simulation result obtained from MATLAB/SIMU-LINK.2.Permanent magnet dc motor and class C chopperA PMDCM fed via class C chopper can be described by the state-space form in the continuous time as follows:where R a is armature resistance (Ohm), L a is armature inductance (Henry), K aψis back electromotive force and torque constant (V/rad/s or Nm/A), J is total moment of inertia (kgm2) and B v is viscous friction constant (Nm/rad/s). V a（t）represents the voltage applied to armature by a class C type of chopper given in Fig. 1.The average value of armature voltage is a function of t on, period of chopping and the level of dc input voltage as shown in Fig. 2.In analog control systems, the repetitive sawtooth waveform is compared with the control voltage to generate the PWM gate signals to the MOSFETs employed in the chopper. The duty cycle is equal to the ratio between control voltage (E c) and the peak of sawtooth. The control voltage signal is generally obtained amplifying the difference between the actual output voltage and its desired value. Simple controls can be carried out using analog IC, such as operational amplifier circuits but sophisticated control tasks usually involves the using of digital ICs, microcontrollers or DSPs to support high-performance, repetitive, numeri-cally intensive tasks. Building a closed-loop control system, the actual output voltage can be sensed by a tacho generator which produces an output voltage proportional to armature rotation. Development and application of FLC in electrical drives have drawn greater attention in recent years (Vas, 1998, 1999).Fig. 1. Dc motor and Class C chopper.Fig. 2. V oltage waveforms of Class C type of chopper.3. Fuzzy controllerConventional controllers are derived from control theory techniques based on mathematical models of the process. They are characterized with design procedures and usually have simple structures. They yield satisfying results and are widely used in industry. However, in a number of cases, such as those, when parameter variations take place, or when disturbances are present, or when there is no simple mathematical model, fuzzy logic based control systems have shown superior performance to those obtained by conventional control algorithms.Fuzzy control is a method based on fuzzylogic. L.A.Zadeh's pioneering work in 1965, and his seminal paper in 1973 on fuzzy algorithms introduced the idea of formulating the control algorithm by logical rules. On the basis of the ideas proposed in this paper, Mamdani developed the first fuzzy control model in 1981. This then led to the industrial applications of fuzzy control.Fuzzy control can be described simplyas "control with sentences rather than equations" (Jan Jantsen1998). It provides an algorithm to convert a linguistic control strategy—based on expertknowledge—into an automatic control strategy. The essential part of a fuzzy controller is a set of linguistic rules which is called rule base.1. If error is Negative and change in error is Negative then output is Negative Big.2. If error is Negative and change in error is Zero then output is Negative Medium.The fuzzy rules are in the familiar if–then format and the "if side" is called the antecedent and the "then side"is called the consequent. The antecedents and the consequents of these if–then rules are associated with fuzzy concepts (linguistic terms), and they are often called fuzzy conditional statements. A fuzzy control rule is a fuzzy conditional statement in which the antecedent is a condition and the consequent is a control action.The fuzzy controller should execute the rules and compute a control signal depending on the measured inputs or conditions. There is no design procedure in fuzzy control such as root-locus design, pole placement design, frequencyresponse design, or stability design because the rules are often non-linear and close to the real world. Non-linearityis handled byrules, member-ship functions and the inference process.Described fuzzy logic model based on non-linear controller is developed and tested on real-time feedback control for the rotor speed of PMDCM. The speed feedback and fuzzy control algorithm block diagram are given in Fig. 3. The tacho-generator measures the actual rotor speed supplying input to the on-chip A/D converter. At the beginning of every kth switching cycle, the reference rotor speed w ref is compared with the actual rotor speed w act. The error (e(k)) and change of error (ce(k)) values of rotor speed are the inputs of the fuzzy control algorithm, which are defined asThe microcontroller calculates these inputs right after conversion from on-chip A/D converter. The fuzzy control algorithm is divided into three modules:(1) fuzzification, (2) decision-making or inference, (3)defuzzification.Fig. 3. Block diagram of drive circuit.In the fuzzification module, the error and change of error signals are evaluated by fuzzy singletons and their numerical values are converted into seven linguistic variables or subsets: PB (Positive Big), PM (Positive Medium), PS (Positive Small), ZE (Zero), NB (Negative Big), NM (Negative Medium) and NS (Negative Small). The fuzzification module calculates the degree of membership of every linguistic variable for given real values of error and change of error. The triangular shapes as given in Fig. 4a are used for smooth operation on membership functions.The calculated values of fuzzy variables are used in he decision-making process. Decision-making is infer-ing from control rules and linguistic variable defidefini-tions. There are seven sets for the error and seven sets for the change of error, and thus total 49 rules taking place for the whole control surface which are given in compact form in Table 1. This rule table can reflect experiences of the human experts.For each error and change of error, there are two overlapping memberships; therefore, all linguistic vari-ables except two has zero membership. Each two overlapping memberships of error and change of error will create four combinations as inference results.The maximum of these four inference results will have two parts, namely, the weighting factor w i and the degree of change of duty cycle y i. The min fuzzy implication rule of Mamdani is used to obtain the weighting factorwhich gives the membership degree of every relation (Lee, 1990). The inferred output ui of each rule isFig. 4. (a) Membership functions for error and change of error.(b) Enlargement for error membership function at a point x.Here, y i represents the centroid of membership function defining the ith rule output variable and can be stored in a look-up table for quick acquisition.Membership functions for change of output of FLC,which is the dutycycle for this application, is shown in Fig. 5According to the membership functions of the input,the output variables and the rule table, respectively, in Figs. 4a, 5 and Table 1, the control surface representa-tion is shown in Fig. 6.In defuzzification module, a crisp value for output is performed. Although the defuzzification process has many methods, the weighted average method is employed for this application because the operation of this method is computationally quite simple and takes less time in the computation process of microcontroller (Bart Kosko,1991; Ross, 1995). The output of defuzzification module can be represented byThe inferred output results and the weighing factors from each of the four rules are used in the equation given above to obtain a crisp value for the change of duty cycle. It is obvious that this calculation is the most time-consuming part of FLC and has computational complexity. The microcontroller output is the PWM duty cycle and defined asThis crisp value is the fuzzy controller actual output at the kth sampling period and is obtained from the previous value of control d(k-1) that is updated by Δd(k).In case of wide range changes of the drive operation, the response of FLC will be non-linear. This means that they should compensate either big positive errors like start-up or small negative errors during step changes.The main difference between the classical PI controller and fuzzy PI controller can be defined with their gains where fuzzy type has variable gain.4.Simulation resultsComputer simulation has an important role in the evaluation of power electronics and closed-loop con-troller designs (Pires and Silva, 2002). Fig. 7 shows the block diagram used in the MATLAB/SIMULINK program to simulate the closed-loop system which was performed on a PMDCM fed via class C chopper, presented in Fig. 3, having the following parameters:R a=0.271Ω,L a=0.41mH, J=0.00074 kgm2, B v=0.0013Nm/rad/s, K aψ= 0.0527V/rad/s.The simulation is performed in the time domain and the sampling period of A/D converter is T =1ms, dc input voltage V =24 and reference speed w ref=188.5 rad/s. The load torque T L was defined as a linear function of rotor speed w r having the operating point (T L =0.26Nm, w r =188.5 rad/s). KE1 in the block diagram given in Fig. 7 is the gain of tacho-generator, W ref1 and W ref , which are the step functions to change the sign of change of error in first sampling. The block of signal generator supplies 2 kHz sawtooth waveform having the magnitude of one; hence, the control signal generated bythe FLC will be equal to the duty cycle of the gate signals applied to the power switches. During simulation process when the change of error is calculated, noise on output of the A/D converter has appeared because change of error in time of 1ms is a very small value, so we have a problem of least significant bit in the number. To eliminate this effect additional gain blocks are introduced before the A/D converter block. So it is necessary to divide after A/D converter output and that is why KE and KCE are placed in the configuration.Fig. 8 shows the variation of actual rotor speed and armature current in time, when the load torque is increased by the amount of 0.17Nm at 0.4 s. The actual rotor speed deviates from the reference speed and comes back to the reference after transients are damped out. The increase of armature current is the response to increase of load torque.5.Hardware and software designsThe hardware setup for the proposed fuzzy logic algorithm was implemented in assembly programming, using 8-bit RISC (Reduced Instruction Set Computing) core microcontroller AT90S8535. A schematic diagram of the FLC with one of the Insulated Gate Bipolar Transistor (IGBT) drive circuits of Class C chopper is shown in Fig. 9. The microcontroller has 8K bytes of programmable flash memory, 512 bytes of internal random access memory, 8 channel 10 bit ADC, 10 bit PWM output, 16 different interrupt sources, an analog comparator, 32 programmable I/O lines, a bi-directional serial interface and has the ability to execute assembler instructions in a single clock cycle. The processing speed is one million instructions per megahertz crystal (Atmel Corporation).The universe of discourse for error and change of error are extended from -1024 to +1024 and the grades of each membership function (0–1) are also extended from 0 to 1024. In order to classify the fuzzy controller inputs, e(k) and ce(k), into seven fuzzy sets and to determine their memberships, 10 bit mathematical routines are used. Therefore, processing the on-chip 10 bit A/D converter sampling values and setting up the on-chip 10 bit PWM output for semiconductor switches are directlyachieved. As a result, the total system resolution is extended to 1/1024 insteadof 1/256 and better performance is obtained.The symmetrical and 50% overlapped triangular membership functions of e(k) and ce(k) simplify the calculations and its negative side is a reflection of the positive. The fuzzy subset linguistic rule table given in Table 1 is changed to integer numbers in order to suit assembly programming. A simple but effective algo-rithm is designed for the appropriate numerical values ofconverted linguistic rules. This is realized with the following equation according to calculated error values.The most important difference between the present paper and the papers cited in the references, is the developed algorithm for fast calculation.For error membership function,where e is the error value, X min=-1024 is the minimum value of the control variable as shown in Fig. 4a, s is the section number, starting from 0 to 6 for representing seven fuzzy subsets of errormembership and T g the width of every triangular membership function, here it is 256.For instance, let w ref =1000 rpm and w act =650 rpm. The calculation of error according to Eq. (2) is e(k)=350 rpm at a point x shown in Fig. 4b and from Eq. (8),is calculated. This value should be classified into fifth fuzzy set. It is well known that every e(k) belongs to at most two fuzzy sets. Therefore, the above error value has two overlapping fuzzy sets, fifth and sixth which are prevailed to linguistic PS and PM as shown in Fig. 4b. The fraction part of s always belongs to the membership function which has a positive slope. Therefore, for this example, the membership grade at point b shown in Fig.4b isμpm(e)=0.36719.The sum of membership grades of two symmetrical and 50% overlapped triangular fuzzy sets is always equal to 1.0μpm(e)+μps(e)=1.0.Hence the membership grade at point a isμps(e)=1.0-0.36719=0.63281.At the kth sampling time, change of error membership function is also evaluated using the same Eq. (8). The calculated values of fuzzy variables are used in the decision-making process. The flow chart of implemented assembler program is shown in Fig. 10.Membership functions of the output duty cycle for application on a PMDCM drive is presented in Fig. 5.A table is created for defuzzification process and stored as a look-up table containing the mean values of these membership functions of output and are used according to the weighted average method for defuzzification. The weighted average method for the defuzzification has an advantage over the other techniques in the case of limited memory structure of RISC core microcontrol-lers. A crisp value for the change of duty cycle is calculated from Eq. (6). According to Eq. (7) and the change of output of FLC shown in Fig. 5, the calculated change of the duty cycle is used to determine the new duty cycle for the application,The calculated value is used to update the PWM output by an interrupt routine shown in Fig. 10 at every 1ms (milli-second). Tests have shown that realization of the mentioned fuzzy interrupt routine lasts 450 ms (micro-second) processing time with 4MHz clock frequency. In this implementation, a D/A converter,which might be needed for output variable is eliminated by directly controlling the on–off periods of semicon-ductor switches via the on-chip PWM generator. When the motor is started from standstill, the error and change of error are estimated as positive at first sampling of rotor speed since their initial value at standstill are taken to be zero. The trend of change of error from standstill to steady state of rotor speed is negative; therefore, by changing the sign of change of error from positive estimated at first sampling to negative reduces the settling time of the rotor speed.6.ResultsAnother identical machine was coupled to the motor via their shafts and operated as a generator for loading. Two different loading conditions were applied on the motor. In the first loading condition, the generator terminals were closed to the resistive load of 2Ωand, the rotor speed and armature current variations in time during this loading case were recorded. These results are given in Fig. 11a (Upper trace: 1V/div., Lower trace: 0.2V/div. and 100mV/A. Time base: 200ms/div.). In the second loading condition, the resistance of 2Ωwas connected to the generator terminals while the rotor is running at reference speed. After the transients were damped out, the resistor was disconnected. The varia-tions of actual rotor speed and generator output current during this case were recorded and are given in Fig. 11b (Upper trace: 1V/div., Lower trace: 0.2V/div. And 100mV/A, Time base: 500ms/div.). It can be observed from the waveforms that the fuzzy logic controller responds to the step change on the load properly and brings the actual rotor speed back to the reference speed.7.ConclusionFuzzy logic controller is implemented without mod-ified methods byusing a general purpose low-cost microcontroller for the speed control of a PMDCM. All the tasks are carried out bya single chip reducing the cost of the system and program code optimization is achieved with developed effective algorithm. In our approach, the software-based decision table is used and only simple computations required in the on-line control of an FLC; therefore, higher sampling rate can be realized more easily when comparing with other type of control schemes. The developed fuzzy logic controller was simulated in MA TLAB/SIMULINK. Simulation and experimental results are compared in order to show the response of the FLC under loading conditions. It can be found that the experimental results are very close to the simulation results. The rise and settling times are reasonably smaller and there is no significant overshoot on the experimental results. The effect of saturation is not included into the motor and generator models; therefore, the simulation results deviate from the experimental results within a small percentage during loading transients. The experiments indicate that the implemented fuzzy controller has a high performance for real time control over a wide range of operating conditions.ReferencesAtmel Corporation. . A VR RISC datasheets, application notes, tools.Bart Kosko, 1991. Neural Networks and FuzzySystems. Prentice-Hall, New York. Brandsetter, P., Sedlak, P., 1996. Fuzzycontrol of electric drive using DSP, PEMC’96, Budapest, Hungary, pp. 3/462–3/466.Gupta, T., Boudreaux, R.R., Nelms, R.M., Hung, J.Y., 1997. Implementation of a fuzzycontroller for DC–DC converter using an inexpensive 8-b Microcontroller. IEEE Transactions on Industrial Electronics 44 (5).Hyo, S.Park, Hee, J.Kim., 2001. Simultaneous control of buck and boost DC–DC converter byfuzzycontroller. ISIE 2001 Proceed-ings, Pusan, Korea, pp. 1021–1025.Jan Jantsen, 1998. Design of FuzzyControllers, Technical Uni-versityof Denmark, Department of Automation, Pub. No: 98-E-864.Lee, C.C., 1990. Fuzzylogic in control systems: fuzzylogic controller.Part I, II. IEEE Transactions on Systems Man and Cybernetics 20(2), 404–435.Mattavelli, P., Rossetto, L., Spiazzi, G., Tenti, P., 1997. General purpose fuzzycontroller for DC–DC converters. IEEE Transac-tions on Power Electronics 12 (1).Pires, V.F., Silva, J.F.A., 2002. Teaching nonlinear modeling, simulation, and control of electronic power converters using.MA TLAB/SIMULINK. IEEE Transactions on Education 45 (3).Ross, T.J., 1995. FuzzyLogic with Engineering Applications.McGraw-Hill, New York.So, W.C., Tse, C.K., Lee, Y.S., 1995. An experimental fuzzy controller for DC–DC converters. IEEE Power Electronics Specialist Con-ference Record. pp. 1339–1345.So, W.C., Tse, C.K., Lee, Y.S., 1996. Development of a fuzzy logiccontroller for DC/DC converters: design, computer simulation and experimental evaluation. IEEE Transactions on Power Electronics11, 1.Vas, P., 1998. Sensorless Vector and Direct Torque Control. Oxford University Press, Oxford. Vas, P., 1999. Artiﬁcial Intelligence Based Electrical Machines and Drives. Oxford UniversityPress, New York.Zakharov, A., 1996. Investigation of dc servo drive with fuzzy logic control. M.Sc. Thesis, Technical Universityof Budapest, Depart-ment of Electrical Machines and Drives.。

专业术语常用名词缩写中英文对照

IC精英博客，交天下IC精英。

IC精英博客的目标是——建立一个IC人的交流平台、网上家园，打造一个IC从业者的圣殿，一个帮助他们成长、提供他们养分的网站。

这个网站不需要管理者，不需要交纳任何费用，完全的Free，自由和自主，有IC各方面的知识技巧，有应用各领域的操作笔记，也有研发最前沿的学术探讨，等等。

IC精英博客的定位于——IC行业精英群体的关注点，他们关心的事情就是我们发掘和宣扬的内容，他们的生活状态和工作环境，也是我们研究和探讨的内容，正是投其所好，对其胃口。

专业术语常用名词缩写中英文对照A:Actuator 执行器A:Amplifier 放大器A:Attendance员工考勤A:Attenuation衰减AA:Antenna amplifier 开线放大器AA:Architectural Acoustics建筑声学AC:Analogue Controller 模拟控制器ACD:Automatic Call Distribution 自动分配话务ACS:Access Control System出入控制系统AD:Addressable Detector地址探测器ADM:Add/Drop Multiplexer分插复用器ADPCM:Adaptive Differential ulse Code Modulation 自适应差分脉冲编码调制AF:Acoustic Feedback 声反馈AFR:Amplitude /Frequency Response 幅频响应AGC:Automati Gain Control自动增益控制AHU:Air Handling Unit 空气处理机组A-I:Auto-iris自动光圈AIS:Alarm Indication Signal 告警指示信号AITS:Acknowledged Information Transfer Service确认操作ALC:Automati Level Control 自动平衡控制ALS:Alarm Seconds 告警秒ALU:Analogue Lines Unit 模拟用户线单元AM:Administration Module管理模块AN:Access Network 接入网ANSI:American National Standards Institute美国国家标准学会APS:Automatic Protection Switching 自动保护倒换ASC:Automati Slope Control 自动斜率控制ATH:Analogue Trunk Unit 模拟中继单元ATM：Asynchrous Transfer Mode 异步传送方式AU- PPJE:AU Pointer Positive Justification 管理单元正指针调整AU:Administration Unit 管理单元AU-AIS:Administrative Unit Alarm Indication SignalAU告警指示信号AUG:Administration Unit Group 管理单元组AU-LOP:Loss of Administrative Unit Pointer AU指针丢失AU-NPJE:AU Pointer Negative Justification管理单元负指针调整AUP:Administration Unit Pointer管理单元指针AVCD:Auchio &Video Control Device 音像控制装置AWG:American Wire Gauge美国线缆规格BA:Bridge Amplifier桥接放大器BAC:Building Automation & Control net建筑物自动化和控制网络BAM:Background Administration Module后管理模块BBER:Background Block Error Ratio背景块误码比BCC:B-channel Connect ControlB通路连接控制BD:Building DistributorBEF:Buiding Entrance Facilities 建筑物入口设施BFOC:Bayonet Fibre Optic Connector大口式光纤连接器BGN:Background Noise背景噪声BGS: Background Sound 背景音响BIP-N:Bit Interleaved Parity N code 比特间插奇偶校验N位码B-ISDN:Brand band ISDN 宽带综合业务数字网B-ISDN:Broad band -Integrated Services Digital Network 宽带综合业务数字网BMC:Burst Mode Controller 突发模式控制器BMS:Building Management System 智能建筑管理系统BRI:Basic Rate ISDN 基本速率的综合业务数字网BS:Base Station基站BSC:Base Station Controller基站控制器BUL：Back up lighting备用照明C/S: Client/Server客户机/服务器C:Combines 混合器C:Container 容器CA:Call Accounting电话自动计费系统CATV:Cable Television 有线电视CC:Call Control 呼叫控制CC:Coax cable 同轴电缆CCD:Charge coupled devices 电荷耦合器件CCF:Cluster Contril Function 簇控制功能CD:Campus Distributor 建筑群配线架CD:Combination detector 感温，感烟复合探测器CDCA:Continuous Dynamic Channel Assign 连续的动态信道分配CDDI:Copper Distributed Data 合同缆分布式数据接口CDES:Carbon dioxide extinguisbing system 二氧化碳系统CDMA:Code Division Multiplex Access 码分多址CF:Core Function 核心功能CFM:Compounded Frequency Modulation 压扩调频繁CIS:Call Information System 呼叫信息系统CISPR:Internation Special Conmittee On Radio Interference 国际无线电干扰专门委员会CLNP:Connectionless Network Protocol 无连接模式网络层协议CLP:Cell Loss Priority信元丢失优先权CM:Communication Module 通信模块CM:Configuration Management 配置管理CM:Cross-connect Matrix交叉连接矩阵CMI:Coded Mark Inversion传号反转码CMISE:Common Management Information Service公用管理信息协议服务单元CPE:Convergence protocol entity 会聚协议实体CR/E:card reader /Encoder (Ticket reader )卡读写器/编码器CRC:Cyclic Redundancy Check 循环冗佘校验CRT:Cathode Ray Tabe 显示器，监视器，阴极射线管CS: Convergence service 会聚服务CS:Cableron Spectrum 旧纳档块化技术CS:Ceiling Screen 挡烟垂壁CS:Convergence Sublayer合聚子层CSC:Combined Speaker Cabinet 组合音响CSCW:Computer supported collaborative work 计算机支持的协同工作CSES:Continuius Severely Errored Second 连续严重误码秒CSF:Cell Site Function 单基站功能控制CTB:Composite Triple Beat 复合三价差拍CTD：Cable Thermal Detector 缆式线型感温探测器CTNR:carrier to noise ratio 载波比CW:Control Word 控制字D:Directional 指向性D:Distortion 失真度D:Distributive 分布式DA:Distribution Amplifier 分配的大器DBA:Database Administrator数据库管理者DBCSN:Database Control System Nucleus数据库控制系统核心DBOS:Database Organizing System 数据库组织系统DBSS:Database Security System 数据库安全系统DC:Door Contacts大门传感器DCC:Digital Communication Channel数字通信通路DCN:Data Communication Network 数据通信网DCP-I:Distributed Control Panel -Intelligent智能型分散控制器DCS:Distributed Control System集散型控制系统DDN:Digital Data Network 数字数据网DDS:Direct Dignital Controller直接数字控制器DDW:Data Describing Word 数据描述字DECT:Digital Enhanced Cordless Telecommunication增强数字无绳通讯DFB:Distributed Feedback 分布反馈DID:Direct Inward Dialing 直接中继方式，呼入直拨到分机用户DLC:Data Link Control Layer 数据链路层DLI:DECT Line InterfaceDODI:Direct Outward Dialing One 一次拨号音DPH:DECT PhoneDRC:Directional Response Cahracteristics 指向性响应DS:Direct Sound 直正声DSP:Digital signal Processing 数字信号处理DSS：Deiision Support System 决策支持系统DTMF:Dual Tone Multi-Frequency 双音多频DTS:Dual -Technology Sensor 双鉴传感器DWDM:Dense Wave-length Division Multiplexing 密集波分复用DXC:Digital Cross-Connect 数字交叉连接E:Emergency lighting照明设备E:Equalizer 均衡器E:Expander 扩展器EA-DFB:Electricity Absorb-Distributed Feedback 电吸收分布反馈ECC:Embedded Control Channel 嵌入或控制通道EDFA:Erbium-Doped Fiber Amplifier掺饵光纤放大器EDI:Electronic Data Interexchange 电子数据交换EIC:Electrical Impedance Characteristics 电阻抗特性EMC：Electro Magnetic Compatibiloty 电磁兼容性EMI:Electro Magnetic Interference 电磁干扰EMS:Electromagnetic Sensitibility 电磁敏感性EN:Equivalent Noise 等效噪声EP:Emergency Power 应急电源ES:Emergency Sooket 应急插座ES:Evacuation Sigvial疏散照明ESA:Error SecondA 误码秒类型ＡESB:ErrorSecondB 误码秒类型ＢESD:Electrostatic Discharge静电放电ESR:Errored Second Ratio 误码秒比率ETDM:Electrical Time Division Multiplexing电时分复用ETSI:European Telecommunication Standards Institute欧洲电信标准协会F:Filter 滤波器FAB:Fire Alarm Bell 火警警铃FACU:Fire Alarm Contrlol Unit 火灾自动报警控制装置FC:Failure Count 失效次数FC:Frequency Converter 频率变换器FCC:Fire Alarm System 火灾报警系统FCS:Field Control System 现场总线FCU：Favn Coil Unit风机盘管FD:Fire Door 防火门FD:Flame Detector 火焰探测器FD:Floor DistributorFD:Frequency Dirsder 分频器FDD:Frequency Division Dual 频分双工FDDI：Fiberdistributed Data Interface光纤缆分布式数据接口。

人工智能的模糊逻辑技术

人工智能的模糊逻辑技术人工智能（Artificial Intelligence）是计算机科学领域中的一个重要研究方向，致力于开发能够模拟人类智能的机器和软件系统。

在人工智能研究中，模糊逻辑技术（Fuzzy Logic）被广泛应用于处理模糊和不确定的信息。

模糊逻辑是一种基于模糊数学的推理方法，用于处理不精确和不完全的信息。

与传统逻辑相比，模糊逻辑能够更好地处理模糊和不确定的情况。

传统逻辑中的命题只有真和假两种取值，而模糊逻辑中的命题可以有一个介于0和1之间的模糊度。

通过引入模糊度的概念，模糊逻辑能够更好地处理现实世界中的不确定性和模糊性。

模糊逻辑的核心思想是模糊集合理论，它将模糊度应用于集合的定义和运算。

传统集合中的元素要么属于集合，要么不属于集合，而模糊集合中的元素可以有不同程度的隶属度。

模糊集合的隶属度可以用一个隶属函数来表示，这个隶属函数可以是一个连续的曲线，描述了元素与集合之间的关系。

在模糊逻辑中，采用模糊规则来推断输出结果。

模糊规则由若干个模糊前提和一个模糊结论组成。

模糊前提是由输入变量的模糊集合和相应的隶属函数描述的，而模糊结论是由输出变量的模糊集合和相应的隶属函数描述的。

推断的过程就是根据输入变量的隶属度和模糊规则的模糊度来计算输出变量的隶属度。

模糊逻辑在人工智能领域的应用非常广泛。

一方面，模糊逻辑能够模拟人类的推理过程，处理模糊和不确定的信息。

例如，在智能控制中，模糊逻辑可以用于建立模糊控制器，根据输入变量和模糊规则来推断输出变量的值，实现对复杂系统的自动控制。

另一方面，模糊逻辑还可以用于模糊分类和模糊聚类问题。

在模糊分类中，通过引入模糊度的概念，模糊逻辑能够更好地处理样本的不确定性和模糊性，提高分类的准确性和鲁棒性。

在模糊聚类中，模糊逻辑可以用于将数据对象划分到不同的模糊簇中，使得相似的对象聚集在一起。

除了在人工智能领域的应用，模糊逻辑还广泛应用于控制工程、模式识别、决策支持系统等领域。

智能科学与技术专业英语

智能科学与技术专业英语一、单词1. Artificial Intelligence (AI)- 英语释义：The theory and development ofputer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision - making, and translation between languages.- 用法：“Artificial Intelligence” is often abbreviated as “AI” and can be used as a subject or in phrases like “AI technology” or “the field of AI”.- 双语例句：- Artificial Intelligence has made great progress in recent years. (近年来，人工智能取得了巨大的进展。

)- Manypanies are investing heavily in artificial intelligence research. (许多公司正在大力投资人工智能研究。

)2. Algorithm- 英语释义：A set ofputational steps and rules for performing a specific task.- 用法：Can be used as a countable noun, e.g. “T his algorithm is very efficient.”- 双语例句：- The new algorithm can solve the problem much faster. (新算法可以更快地解决这个问题。

机械毕业设计英文外文翻译235基于模糊逻辑的AMT离合器结合控制研究

附录A 外文文献Study of Controlling Clutch Engagement forAMT Based on Fuzzy LogicTAN G Xia-qing , HOU Chao-zhen , CHEN Yun-chuangAbstract: The control of the clutch engagement for an automatic mechanical transmission in the process of a tracklayer getting to start is studied. The dynamic model of power transmission and automatic clutch system is developed. Using tools of Simulink , the transient characteristics during the vehicle starting , including the jerk and the clutch slip time , are provided here. Based on the analyses of the simulation results and driver’s experiences , a fuzzy controller is designed to control the clutch engagement . Simulation results verify its value.Key words : clutch ; automatic transmission ; fuzzy controlThe automatic mechanical transmission (AMT) has several advantages , such as simpleness , higher efficiency and lower costs. But these benefits come from settling a series of challenging control problem. For example , it is difficult andcomplex for an AMT to properly control the clutch engagement while the vehicle starting , because different drivers have different intentions ( for example smooth start and fast start) , the second cause is that the control goals of lengthening the clutch life and smoothly starting vehicle are contradictory. So it is an important research field for AMT , and some researchers are studying this problem too.The focus of this paper is to study the control of clutch engagement for AMT while the tracklayer starts. In most cases , experiment methods are used to improve starting quality , however , they require much effort and time to develop a new control algorithm and to investigate the effect of this design. On the contrary , a simulation method has the merit of saving money and time , and overcomes the restrictions of experimental conditions. The fuzzy controller is designed based on the analysis of the simulation results and dr iver’s experience.The organization of the paper is as follows. First , the system model is described , along with some simulation results. Secondly , the fuzzy control strategy of clutch engagement is developed. Finally ,conclusions from this work , as well as recommendation for future work , are also outlined.The control goal of the clutch control system is to ensure the vehicle starting according to driver’s intention and make the clutch engage smoothly and the jerk as small as possible. Based on the analysis of the simulation results and driver’s experience , we have the following conclusions.①The accelerator pedal βindicates driver’s intention and his judgement on the environment and vehicle’s states. The larger βis , the higher the engaging speed vshould be.com②The engine rotational speed ωe indicates its carrying capacity. The larger ωe is , the stronger the carrying capacity is.③The r is the speed ratio between the passive and the active departments of the clutch which can be expressed by r =ωc/ωe. It indicates the slip state of the clutch. The larger r is ,should be. Consequently , the higher the engaging speed vcomthe control strategy is expressed as follows :①Regulate the engine rotational speed ωes according to the signal of the accelerator pedal βbefore engaging the clutch. The larger βis , the higher ωes should be.is ②While engaging the clutch , the engaging speed vcom decided by the accelerator pedal β, the engine rotationalspeed ωe and the speed ratio r by using fuzzy logic.③Regulate the throttle opening as the driver regulates the accelerator pedal β.The fuzzy logic approach is used here to control the engaging speed of the clutch .The engine rotational speed ωe and engaging speed of the clutch vcom are normalized. Following this method , the i-th control rule can be= Dj . written as Ri : If β= Aj and r = Bj and ωe = Ci then vcomHere , A j is the fuzzy set of the accelerator pedal β, Bj is the fuzzy set of the engine rotational speed ωe , Cj is the fuzzy set of the speed ratio r and Dj is the fuzzy set of the clutch.engaging speed vcomConclusionThe key point of the vehicle start is to accomplish the driver’s intention and ensure the vehicle starting smoothly. The model results can be used to study the clutch engagement . To overcome the difficulty of clutch control , a fuzzy control strategy is proposed based on the states of accelerator pedal β, engine rational speed ωand speed ratio r. Simulationeindicates it is valuable. The future work is to optimize the parameters of the membership function in experiment and test its effects.附录B 外文文献翻译基于模糊逻辑的AMT离合器结合控制研究汤霞清, 侯朝桢, 陈云窗摘要:研究具有机械式自动变速器的履带式车辆起步时离合器结合控制问题。

传感器英文文献

Fuzzy logic control of wind energy conversion systemHassan M. Farh and Ali M. EltamalyCitation: Journal of Renewable and Sustainable Energy 5, 023125 (2013); doi: 10.1063/1.4798739View online: /10.1063/1.4798739View Table of Contents: /content/aip/journal/jrse/5/2?ver=pdfcovPublished by the AIP PublishingArticles you may be interested inNew application of predictive direct torque control in permanent magnet synchronous generator-based wind turbineJ. Renewable Sustainable Energy 7, 023108 (2015); 10.1063/1.4915261Power quality assessment of a solar photovoltaic two-stage grid connected system: Using fuzzy and proportional integral controlled dynamic voltage restorer approachJ. Renewable Sustainable Energy 7, 013113 (2015); 10.1063/1.4906980A new hybrid control method for controlling back-to-back converter in permanent magnet synchronous generator wind turbinesJ. Renewable Sustainable Energy 6, 033133 (2014); 10.1063/1.4884198New methodology of speed-control of photovoltaic pumping systemJ. Renewable Sustainable Energy 5, 053109 (2013); 10.1063/1.4821213Recurrent modified Elman neural network control of permanent magnet synchronous generator system based on wind turbine emulatorJ. Renewable Sustainable Energy 5, 053103 (2013); 10.1063/1.4811792Fuzzy logic control of wind energy conversion systemHassan M.Farh 1,a)and Ali M.Eltamaly 2,a)1Department of Electrical Engineering,College of Engineering,King Saud University,P.O.Box 800,Riyadh 11421,Saudi Arabia 2Sustainable Energy Technologies Center,Department of Electrical Engineering,College of Engineering,King Saud university,Riyadh 11421,Saudi Arabia(Received 4November 2012;accepted 11March 2013;published online 3April 2013)This paper proposes a variable speed control scheme of grid-connected windenergy conversion system,WECS,using permanent magnet synchronousgenerator.The control algorithm tracking the maximum power for wind speedsbelow rated speed of wind turbines (WTs)and ensure the power will not exceedthe rated power for wind speeds higher than the rated speed of wind turbine.Thecontrol algorithm employed fuzzy logic controller (FLC)to effectively do thisjob.The WT is connected to the grid via back-to-back pulse width modulation-voltage source converter (PWM-VSC).Two effective computer simulationsoftware packages (PSIM and SIMULINK)have been used to carry out thesimulation effectively where PSIM contains the power circuit of the WECS andMATLAB/SIMULINK contains the control circuit of the system.The controlsystem has two controllers for generator side and grid side converters.The mainfunction of the generator side controller is to track the maximum power fromwind through controlling the rotational speed of the turbine using FLC.In thegrid side converter,active and reactive power control has been achieved bycontrolling d-axis and q-axis current components,respectively.VC 2013American Institute of Physics .[/10.1063/1.4798739]I.INTRODUCTIONWind is one of the most promising renewable energy resources for producing electricitydue to its cost competitiveness compared to other conventional types of energy resources.Ittakes a particular place to be the most suitable renewable energy resources for electricity pro-duction.It isn’t harmful to the environment and it is an abundant resource available in nature.Hence,wind power could be utilized by mechanically converting it to electrical power usingwind turbines (WTs).Various WT concepts have a quick development of wind power technolo-gies and signiﬁcant growth of wind power capacity during last two decades.Variable speedoperation and direct drive (DD)WTs have been the modern developments in the technology ofwind energy conversion system (WECS).Variable-speed operation has many advantages overﬁxed-speed generation such as increased energy capture,operation at MPPT over a wide rangeof wind speeds,high power quality,reduced mechanical stresses,and aerodynamic noiseimproved system reliability,and it can provide (10%–15%higher output power and has lessmechanical stresses when compared with the operation at a ﬁxed speed.1,2WTs can be classiﬁed,according to the type of drive train,into DD and gear drive (GD).The GD type uses a gear box,squirrel cage induction generator,SCIG,and classiﬁed as stall,active stall,and pitch control WT,and work in constant speed applications.The variable speedWT uses doubly fed induction generator,DFIG,especially in high power WTs.The gearlessDD and WTs have been used with small and medium size WTs employing permanent magnetsynchronous generator (PMSG)with higher numbers of poles to eliminate the need for gearboxa)Authors to whom correspondence should be addressed.Electronic addresses:eltamaly@.sa (Tel.:þ966553334130)and hfarh1@.sa (Tel.:þ966500507630).Fax:þ96614676757.1941-7012/2013/5(2)/023125/13/$30.00V C 2013American Institute of Physics 5,023125-1JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 5,023125(2013)which can be translated to higher efﬁciency.PMSG appears more and more attractive,because of the advantages of permanent magnet,PM machines over electrically excited machines such as its higher efﬁciency,higher energy yield,no additional power supply for the magnetﬁeld excitation,and higher reliability due to the absence of mechanical components such as slip rings.In addition,the performance of PM materials is improving,and the cost is decreasing in recent years.Therefore,these advantages make direct-drive PM wind turbine generator systems more attractive in application of small and medium-scale wind turbines.1,3,4Robust controller has been developed in many literatures5–15to track the maximum power available in the wind.They include tip speed ratio,TSR,5,13power signal feedback,PSF,8,14 and the hill-climb searching,HCS11,12methods.The TSR control method regulates the rota-tional speed of the generator to maintain an optimal TSR at which power extracted is maxi-mum.13For TSR calculation,both the wind speed and turbine speed need to be measured,and the optimal TSR must be given to the controller.Theﬁrst barrier to implement TSR control is the wind speed measurement,which adds to system cost and presents difﬁculties in practical implementations.The second barrier is the need to obtain the optimal value of TSR;this value is different from one system to another.This depends on the turbine-generator characteristics that result in custom-designed control software tailored for individual wind turbines.14In PSF control,8,14it is required to have the knowledge of the wind turbine’s maximum power curve, and track this curve through its control mechanisms.The power curves need to be obtained via simulations or off-line experiment on individual wind turbines or from the datasheet of WT which makes it difﬁcult to implement with accuracy in practical applications.7,8,15The HCS technique does not require the data of wind,generator speeds and the turbine characteristics. But,this method works well only for very small wind turbine inertia.For large inertia wind tur-bines,the system output power is interlaced with the turbine mechanical power and rate of change in the mechanically stored energy,which often renders the HCS method ineffective.11,12 On the other hand,different algorithms have been used for maximum power extraction from WT in addition to the three methods mentioned above.For example,Oghafy and Nikkhajoei1presents an algorithm for maximum power extraction and reactive power control of an inverter through the power angle,d of the inverter terminal voltage and the modulation index,m a based variable-speed WT without wind speed sensor.Chinchilla et al.16present an algorithm for MPPT via controlling the generator torque through q-axis current and,hence,con-trolling the generator speed with variation of the wind speed.These techniques are used for a decoupled control of the active and reactive power from the WT through q-axis and d-axis cur-rent,respectively.Also,Song et al.17present a decoupled control of the active and reactive power from the WT,independently through q-axis and d-axis current but maximum power point operation of the WECS has been produced through regulating the input dc current of the dc/dc boost converter to follow the optimized current reference.Eltamaly18presents an algorithm for MPPT through directly adjusting duty ratio of the dc/dc boost converter and modulation index of the PWM-VSC.Hussein et al.19present MPPT control algorithm based on measuring the dc-link voltage and current of the uncontrolled rectiﬁer to attain the maximum available power from wind.Finally,MPPT control based on fuzzy logic controller(FLC)has been presented in (Pati and Sahu;Yao and Liu;Abo-Khalil and Seok).20–22The function of FLC is to track the generator speed with the reference speed for maximum power extraction at variable speeds.In this study,the WECS is designed as PMSG connected to the grid via a back-to-back PWM-VSC as shown in Fig.1.MPPT control algorithm has been introduced using FLC to reg-ulate the rotational speed to force the PMSG to work around its maximum power point in speeds below rated speeds and to produce the rated power in wind speed higher than the rated wind speed of the WT.Indirect vector-controlled PMSG system has been used for this purpose. The input to FLC is two real time measurements which are the change of output power and rotational speed between two consequent iterations(D P m and D x m).The output from FLC is the required change in the rotational speed D x m*.The detailed logic behind the new proposed technique is explained in detail in the following sections.Two effective computer simulation software packages(PSIM and SIMULINK)have been integrated to carry out the simulation effectively.PSIM contains the power circuit of the WECS and MATLAB/SIMULINK containsthe control circuit of the system.The idea behind integrating these two different software pack-ages is the effective tools provided with PSIM for power circuit and the effective tools inSIMULINK for control circuit and FLC.This integration between PSIM and SIMULINK has neverbeen used in MPPT of wind energy systems in the literature and this approach will helpresearchers to develop many other control techniques in this area.The interconnection betweenPSIM and SIMULINK makes the simulation process easier,efﬁcient,fast response,and powerful.In the grid side converter,active and reactive power control has been achieved by controllingd-axis and q-axis grid current components,respectively.The q-axis grid current is controlled tobe zero for unity power factor and the d-axis grid current is controlled to deliver the powerﬂowing from the dc-link to the grid.II.WIND ENERGY CONVERSION SYSTEM DESCRIPTIONFig.2shows a co-simulation (PSIM/SIMULINK)program for interconnecting WECS toelectric utility.The PSIM program contains the power circuit of the WECS and MATLAB /SIMULINK program contains the control of this system.The interconnection between PSIM andMATLAB /SIMULINK has been done via the SimCoupler block.The basic topology of the power cir-cuit,which has PMSG driven wind turbine connected to the utility grid through the ac-dc-acconversion system,is shown in Fig.1.The PMSG is connected to the grid through back-to-back bidirectional PWM voltage source converters VSC.The generator side converter is con-nected to the grid side converter through dc-link capacitor.The control of the overall systemhas been done through the generator side converter and the grid side converter.MPPTalgorithm has been achieved through controlling the generator side converter using FLC.TheFIG.1.Schematic diagram of the overallsystem.FIG.2.Co-simulation block of wind energy system interfaced to electric utility.grid-side converter controller maintains the dc-link voltage at the desired value by exporting active power to the grid and it controls the reactive power exchange with the grid.A.Wind turbine modelWind turbine converts the wind power to a mechanical power.This mechanical power gen-erated by wind turbine at the shaft of the generator can be expressed asP m¼12C Pðk;bÞq A u3;(1)where q is the air density(typically1.225kg/m3),b is the pitch angle(in degree),A is the area swept by the rotor blades(in m2);u is the wind speed(in m/s),and C p(k,b)is the wind-turbine power coefﬁcient(dimensionless).The turbine power coefﬁcient,C p(k,b),describes the power extraction efﬁciency of the wind turbine and is deﬁned as the ratio between the mechanical power available at the turbine shaft and the power available in wind.A generic equation is used to model C p(k,b).This equa-tion,based on the modeling turbine characteristics,is shown as follows:23C Pðk;bÞ¼0:5176116Ã1k iÀ0:4bÀ5eÀ21k iþ0:0068k;(2)with 1k i¼1kþ0:08bÀ0:0351þb3;where C P is a nonlinear function of both tip speed ratio,k and the blade pitch angle,b.k is the ratio of the turbine tip speed,x m*R to the wind speed,u.k is deﬁned as24k¼x mÃR;(3)where x m is the rotational speed and R is the turbine blade radius,respectively.For aﬁxed pitch angle,b,C P becomes a nonlinear function of k only.According to Eq.(3),there is a relation between k and x m.Hence,at a certain u,the power is maximized at a certain x m called opti-mum rotational speed,x opt.This speed corresponds to optimum tip speed ratio,k opt.15The value of the tip speed ratio is constant for all maximum power points.So,to extract maximum power at variable wind speed,the WT should always operate at k opt in speeds below the rated speed.This occurs by controlling the rotational speed of the WT to be equal to the optimum rotational speed.Fig.3shows that the mechanical power generated by WT at the shaft of the generator as a function of x m.These curves have been extracted from PSIM support team for the wind turbine used in this paper.It is clear from thisﬁgure that for each wind speed the me-chanical output power is maximized at particular rotational speed,x opt,as shown in Fig.3.B.PMSG modelThe generator is modeled by the following voltage equations in the rotor reference frame (dq-axes):25v sd¼R s i sdþd k sddtÀx r k sqv sq¼R s i sqþd k sqdtþx r k sd;(4)where k sq and k sd are the statorﬂux linkages in the direct and quadrature axis of rotor which in the absence of damper circuits can be expressed in terms of the stator currents and the magnetic ﬂux as follows:25k sd ¼L s i sd þw Fk sq ¼L s i sq ;(5)where w F is the ﬂux of the permanent magnets.The electrical torque,T e ,of the three-phasegenerator can be calculated as follows:25,26T e ¼32P ½k sd i sq Àk sq i sd ;(6)where P is the number of pole pairs.For a non-salient-pole machine,the stator inductances L sdand L sq are approximately equal.25This means that the direct-axis current i sd does not contrib-ute to the electrical torque.Our concept is to keep i sd to zero in order to obtain maximal torquewith minimum current.Then,the electromagnetic torque results,T e ¼32P w F i sq ¼K c i sq ;(7)where i sq is the quadrature-axis component of the stator-current space vector expressed in therotor reference frame and K c is called the torque constant and represents the proportional coefﬁ-cient between T e and i sq .III.CONTROL OF THE GENERATOR SIDE CONVERTERThe generator side controller controls the rotational speed to produce the maximum outputpower via controlling the electromagnetic torque according to Eq.(7),where the indirect vectorcontrol is used.The proposed control logic of the generator side converter is shown in Fig.4.The speed loop will generate the q-axis current component to control the generator torqueand speed at different wind speed via estimating the references value of i a and i b as shown inFig.4.The torque control can be achieved through the control of the i sq current as shown inEq.(7).Fig.5shows the stator and rotor current space phasors and the excitation ﬂux of thePMSG.25The quadrature stator current,i sq ,can be controlled through the rotor reference frame(a ,b axes)as shown in Fig.5.So,the references value of i a and i b can be estimated easilyfrom the amplitude of i sq*and the rotor angle,⍜r .Initially,to ﬁnd the rotor angle,⍜r ,the rela-tionship between the electrical angular speed,x r ,and the rotor mechanical speed (rad/s),x mmay be expressedasFIG.3.Typical output power characteristics.x r ¼P 2x m :(8)So,the rotor angle,⍜r ,can be estimated by integrating of the electrical angular speed,x r .Theinput to the speed control is the actual and reference rotor mechanical speed (rad/s)and the out-put is the (a ,b )reference current components.The actual values of the (a ,b )current compo-nents are estimated using Clark’s transformation to the three phase current of PMSG.The FLCcan be used to ﬁnd the reference speed along which tracks the maximum power point.IV.FUZZY LOGIC CONTROLLER FOR MPPTAt certain wind speed,the power is maximized at a certain x called optimum rotationalspeed,x opt .This speed corresponds to optimum tip speed ratio,k opt .15So,to extractmaximumFIG.4.Control block diagram of the generator sideconverter.FIG.5.The stator and rotor current space phasors and the excitation ﬂux of the PMSG.power at variable wind speed,the turbine should always operate at k opt.This occurs by control-ling the rotational speed of the turbine.Controlling of the turbine to operate at optimum rota-tional speed can be done using the FLC.Each wind turbine has one value of k opt at variable speed but x opt changes from a certain wind speed to another.From Eq.(3),the relation between x opt and wind speed,u,for constants R and k opt can be obtained as follows:x opt¼k optRu:(9)From Eq.(9),the relation between the optimum rotational speed and wind speed is linear. FLC is used to search the rotational speed reference which tracks the maximum power point at variable wind speeds.The block diagram of FLC is shown in Fig.6.Two variables are used as input to FLC(D P m and D x m)and the output is(D x m*).Membership functions are shown in Fig.7.Triangular symmetrical membership functions are suitable for the input and output, which give more sensitivity especially as variables approach to zero value.FLC does not require any detailed mathematical model of the system and its operation is governed simply by a set of rules.The principle of the FLC is to perturb the reference speed,x m*and to observe the corresponding change of power,D P m.If the output power increases with the last speed increment,the searching process continues in the same direction.On the other hand,if the speed increment reduces the output power,the direction of the searching is reversed.The FLC is efﬁcient to track the maximum power point,especially in case of frequently changing wind conditions.22The input and output membership functions have been shown in Fig.7.The control rule for input and output variables are listed in Table I.D x m is varied fromÀ0.15rad/s to0.15rad/s and D P m is varied fromÀ30W to30W.The membership deﬁnitions are used as follows:N (negative),NB(negative big),NS(negative small),ZE(zero),P(positive),PS(positive small), and PB(positive big).V.CONTROL OF THE GRID SIDE CONVERTERThe powerﬂow of the grid-side converter is controlled in order to maintain the dc-link voltage at reference value,600V.Since increasing the output power than the input power to dc-link capacitor causes a decrease of the dc-link voltage and vise versa,the output power will be regulated to keep dc-link voltage approximately constant.The dc-link voltage has been maintained and the reactive powerﬂowing into the grid has been controlled at zero value.This has been done via controlling the grid side converter currents using the d-q vector control approach.By aligning the d-axis of the reference frame along with the grid voltage position v q¼0and then the active and reactive power can be obtained from the following equations:FIG.6.Input and output of fuzzy controller.P s ¼32v d i d ;(10)Q s ¼32v d i q :(11)Active and reactive power control has been achieved by controlling d-axis and q-axis cur-rent components,respectively,using two control loops.An outer dc-link voltage control loopis used to set the d-axis current reference for active power control.The inner control loop con-trols the reactive power by setting the q-axis current reference to zero value for unity powerfactor as shown in Eq.(11).The control block diagram of the grid side converter is shown inFig.8.VI.SIMULATION RESULTSTwo effective computer simulation software packages (PSIM and SIMULINK)have beenintegrated together to carry out the simulation of the modiﬁed system effectively.The modelof WECS (power circuit)in PSIM contains the WT connected to the utility grid through back-to-back bidirectional PWM converter.The control of whole system in SIMULINK contains thegenerator side controller and the grid side controller.The idea behind integrating these twodifferent software packages is that PSIM is a very effective and a simple tool for modeling thepower electronics circuits whereas SIMULINK is a very effective and a simple tool for model-ing the control system especially for FLC and mathematical manipulation.The windturbineFIG.7.Membership functions of FLC.TABLE I.Rules of FLC.D P m /D x mNB NS ZE PS PB NPB PS ZE NS NB ZENM NS ZE PS PM P NB NS ZE PM PBcharacteristics and the parameters of the PMSG are listed in the Appendix.The generator can be directly controlled by the generator side controller to track the maximum power available from the WT.To extract maximum power at variable wind speed,the turbine should always operate at k opt.This occurs by controlling the rotational speed of the WT.So,it always oper-ates at the optimum rotational speed,x opt,for different wind speed.The fuzzy logic controller is used to search the optimum rotational speed which tracks the maximum power point at vari-able wind speeds.The proposed system has been compared with the system shown in Ref.22to validate the results.The whole simulation results of the proposed system and results from Ref.22are shown in Fig.9in left hand side(LHS)and right hand side(RHS),respectively.The input wind speeds have been assumed to be saw-tooth as the wind speed in Ref.22for easy compari-son.Figs.9(a1)and9(a2)show the input wind speed variation for the proposed system and the system shown in Ref.22,respectively.Reference rotational speed for the proposed system and the system shown in Ref.22are shown in Figs.9(b1)and9(b2),respectively.At a certain wind speed,the actual and reference rotational speed have been estimated and this agrees with the power characteristic of the wind turbine shown later in Fig.3(i.e.,the WT always operates at the optimum rotational speed which can be obtained from the output of FLC).Figs.9(c1)and 9(c2)show the variation of the actual rotational speed for the proposed system and the system shown in Ref.22,respectively.It is clear from Figs.9(c1)and9(c2)that the rotational speed variation of the proposed system follows the reference rotational speed strictly without noise in the waveform.Figs.9(d1)and9(d2)show the active power extraction from the proposed system and the system shown in Ref.22,respectively.It is clear from thisﬁgure that the output power generated from the proposed system is higher and follows strictly the maximum power ofFIG.8.Control block diagram of grid-side converter.Fig.3than the output power from the system shown in Ref.22.The reference value of the reactive power has been set at zero value.Figs.9(e1)and9(e2)show the waveform of the actual reactive power for the proposed system and the system shown in Ref.22,respectively.It is clear from thisﬁgure that the reactive power obtained from the proposed system is strictly following the reference value without spikes more than the one obtained from the system shown in Ref.22.Also,the dc-link voltage has been set at600V in both systems.The actual dc-link voltage waveforms are shown in Figs.9(f1)and9(f2)for the proposed system and the system shown in Ref.22,respectively.It is clear that the dc-link voltage obtained from the proposed system is following the reference value strictly more than the one obtained from the system shown in Ref.22.It is clear from the above discussion that the proposed system in this paper is superior and showed a stable operation and followed strictly the reference values of rotational speed,reactive power,and the dc-link voltage.Also,it is clear from the simulation results of the proposed sys-tem that the maximum power has been extracted strictly as the maximum power obtained from Fig.3.FIG.9.Different simulation waveforms of prposed system in LHS compared to the same waveforms obtained from Ref.22 in RHS:(a)Wind speed variation,(b)reference rotational speed(rad/s),(c)actual rotational speed(rad/s),(d)active power (W),(e)reactive power(Var),and(f)dc-link volltage(V).VII.CONCLUSIONA co-simulation (PSIM/SIMULINK)program has been proposed for WECS where PSIMcontains the power circuit of the WECS and MATLAB /SIMULINK contains the control circuit of theWECS.The integration between PSIM and SIMULINK is the ﬁrst time to be used in modelingWECS which help researchers in modifying the modeling of WECS in the future.The intercon-nection between PSIM and SIMULINK makes the simulation process easier,efﬁcient,fastresponse and powerful.The WT is connected to the grid via back-to-back PWM-VSC.The gen-erator side controller and the grid side controller have been done in SIMULINK .The main func-tion of the generator side controller is to track the maximum power from wind through control-ling the rotational speed of the turbine using fuzzy logic controller.The fuzzy logic algorithmfor the maximum output power of the grid-connected wind power generation system using aPMSG has been proposed and implemented above.The PMSG was controlled in indirect-vectorﬁeld oriented control method and its speed reference was determined using fuzzy logic control-ler.In the grid side converter,active and reactive power control has been achieved by control-ling d-axis and q-axis grid current components,respectively.The q-axis grid current is con-trolled to be zero for unity power factor and the d-axis grid current is controlled to delivertheFIG.9.(Continued).power ﬂowing from the dc-link to the grid.The simulation results prove the superiority of FLCand the whole control system.ACKNOWLEDGMENTSThe authors acknowledge the College of Engineering Research Center and Deanship ofScientiﬁc Research at King Saud University in Riyadh for the ﬁnancial support to carry out theresearch work reported in this paper.APPENDIX:PARAMETERS OF WT MODEL AND PMSG1V.Oghafy and H.Nikkhajoei,“Maximum power extraction for a wind-turbine generator with no wind speed sensor,”inProceedings on IEEE,Conversion and Delivery of Electrical Energy in the 21st Century (2008),pp.1–6.2T.Ackerman and L.S €o der,“An overview of wind energy status 2002,”Renewable sustainable Energy Rev.6,67–128(2002).3M.R.Dubois,“Optimized permanent magnet generator topologies for direct-drive wind turbines,”Ph.D.dissertation(Delft University of Technology,Delft,the Netherlands,2004).4A.Grauers,“Design of direct-driven permanent-magnet generators for wind turbines,”Ph.D.dissertation (ChalmersUniversity Technology,Goteborg,Sweden,1996).5T.Thiringer and J.Linders,“Control by variable rotor speed of a ﬁxed pitch wind turbine operating in a wide speedrange,”IEEE Trans.Energy Convers.EC-8,520–526(1993).6I.K.Buehring and L.L.Freris,“Control policies for wind energy conversion system,”IEE Proceedings C:Generation,Transmission &Distribution,128,253–261(1981).7M.Erimis,H.B.Ertan,E.Akpinar,and F.Ulgut,“Autonomous wind energy conversion systems with a simple controllerfor maximum power transfer,”IEE Proceedings B:Electric Power Applications 139,421–428(1992).8R.Chedid,F.Mrad,and M.Basma,“Intelligent control of a class of wind energy conversion systems,”IEEE Trans.Energy Convers.EC-14,1597–1604(1999).9M.G.Simoes,B.K.Bose,and R.J.Spiegal,“Fuzzy logic-based intelligent control of a variable speed cage machinewind generation system,”IEEE Trans.Power Electron.PE-12,87–94(1997).10J.H.Enslin and J.V.Wyk,“A study of a wind power converter with micro-computer based maximum power control uti-lizing an over-synchronous electronic scherbius cascade,”Renewable Energy World 2(6),551–562(1992).11Q.Wang and L.Chang,“An intelligent maximum power extraction algorithm for inverter-based variable speed wind tur-bine systems,”IEEE Trans.Power Electron.19(5),1242–1249(2004).12Q.Wang,“Maximum wind energy extraction strategies using power electronic converters,”Ph.D.dissertation(University of New Brunswick,Canada,2003).13H.Li,K.L.Shi,and P.G.McLaren,“Neural-network-based sensorless maximum wind energy capture with compensatedpower coefﬁcient,”IEEE Trans.Ind.Appl.41(6),1548–1556(2005).14A.B.Raju,B.G.Fernandes,and K.Chatterjee,“A UPF power conditioner with maximum power point tracker for gridconnected variable speed wind energy conversion system,”in Proceedings of 1st International Conference on PESA,Bombay,India,9-11November (2004),pp.107–112.15M.A.Abdullah,A.H.M.Yatim,and C.W.Tan,“A study of maximum power point tracking algorithms for wind energysystem,”in Procedings of 1st IEEE Conference on Clean Energy and Technology CET,2011.16M.Chinchilla,S.Arnaltes,and J.C.Burgos,“Control of permanent-magnet generators applied to variable-speed wind-energy systems connected to the grid,”IEEE Trans.Energy Convers.21(1),130–135(2006).17S.Song,S.Kang,and N.Hahm,“Implementation and control of grid connected AC-DC-AC power converter for variablespeed wind energy conversion system,”in Applied Power Electronics Conference and Exposition,IEEE,2003.18A.M.Eltamaly,“Modelling of wind turbine driving permanent Magnet Generator with maximum power point trackingsystem,”J.King Saud Univ.19(2),223–237(2007).19M.M.Hussein,M.Orabi,M.E.Ahmed,and M.A.Sayed,“Simple sensorless control technique of permanent magnetsynchronous generator wind turbine,”in Proceedings of IEEE International Conference on Power and Energy(PEC2010),Kuala Lumpur,Malaysia (2010),pp.512–517.TABLE II.Parameters of wind turbine model and PMSG.Wind turbinePMSG Nominal output power19kW R s (stator resistance)1m Wind speed input8:12m/s (saw tooth)L d (d-axis inductance)1m Base wind speed10m/s L q (q-axis inductance)1m Base rotational speeds190rpm No.of poles P 30Moment of inertia1m Moment of inertia 100m Blade pitch angle input 0 Mech.time constant 1。

1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。

My Understanding about Fuzzy Logic
When it comes to fuzzy logic, there are different kinds of definitions and understanding about this concept. However, in essence, I think，these definitions and understanding are similar. Because the fuzzy is based on the uncertainty of abstract thinking and concept, as well as the imprecise nature of things. As my understanding of fuzzy logic is superficial, so I have to use a relatively perfect definition to express my thought.
In narrow sense: Fuzzy logic is a logical system, which is an extension of multi-valued logic.
In a wider sense: Fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects with unsharp boundaries in which membership is a matter of degree.
----- by Mahesh Todkar Fuzzy logic is not the unclear logic. Actually, it is founded on the fuzzy set, which was put forward by Pro. Zadeh in 1965. Then Zadeh developed fuzzy logic as a way of processing data. Instead of requiring a data element to be either a member or non-member of a set, he introduced the idea of partial set membership.
Fuzzy logic is a method between the symbolic reasoning of traditional artificial intelligence and numerical computing theory of the conventional control. It does not rely on the model, it uses linguistic variables to represent the abstract variables and uses rules for fuzzy reasoning and processing. Moreover, it is also featured in its recognition of the intermediate transitional between true value ( True ) and false value ( False ).
Hence, the most essential concept for fuzzy logic is the membership function, which defines how each point in the input space is mapped to a membership value between 0 and 1. The membership function is denoted by μ and also called as degree of membership or membership grade or degree of truth of proposal. There are many types of membership functions, like Piece-wise linear functions, Gaussian distribution function, Sigmoid curve and Singleton Membership Function etc.
In addition, we should pay the major attention to the fuzzy inference, which is the process of formulating the mapping from a given input to an output using fuzzy logic.
It involves Membership Functions (MF), Logical Operators and If-Then Rules. The MF is mentioned above, so an introduction about Logical Operators and If-Then Rules will be presented as followed.
Fuzzy Logic Operators are used to write logic combinations between fuzzy notions.
As for Zadeh operators, its definitions are :
1)Intersection: μ(A AND B) = MIN(μ(A), μ(B))
2)Union: μ(A OR B) = MAX(μ(A), μ(B))
3)Negation: μ(NOT A) = 1 -μ(A)
Fuzzy If-Then Rules are the statements used to formulate the conditional statements that comprise fuzzy logic. For example:
if x is A then y is B
where,
A &
B – Linguistic values x – Element of Fuzzy set X y – Element of Fuzzy set Y
In above example,
Antecedent (or Premise)– if part of rule (i.e. x is A)
Consequent (or Conclusion) – then part of rule (i.e. y is B)
Here, interpreting if-then rule is a three–part process:
1) Fuzzify input:
Resolve all fuzzy statements in the antecedent to a degree of membership between 0 and 1.
2) Apply fuzzy logic operator to multiple part antecedents:
If there are multiple parts to the antecedent, apply fuzzy logic operators and resolve the antecedent to a single number between 0 and 1.
3) Apply implication method:
The output fuzzy sets for each rule are aggregated into a single output fuzzy set. Then the resulting output fuzzy set is defuzzified, or resolved to a single number.
In general, from my perspective, compared with conventional binary logic, fuzzy logic is a breakthrough for the classification of things. To some degree, fuzzy logic makes the uncertainty and imprecision clearer. Though the membership functions vary from person to person, which indicates that fuzzy logic is subjective, its advantages are explicit. Just as
Mr. Hu Baoqing(from Wuhan University) notes that Benefits of Fuzzy Mathematics are:
①The ability to model highly complex business problems
②Improved cognitive modeling of expert system
③The ability to model systems involving multiple experts
④Reduced model complexity
⑤Improved handling of uncertainty and possibilities
……。