模糊控制的理论与发展概述—毕业论文

1、模糊控制考核论文模糊控制的理论与发展概述摘要 模糊控制理论是以模糊数学为基础,用语言规则表示方法和先进的计算机技术,由模糊推理进行决策的一种高级控制策。模糊控制作为以模糊集合论、模糊语言变量及模糊逻辑推理为基础的一种计算机数字控制，它已成为目前实现智能控制的一种重要而又有效的形式尤其是模糊控制和神经网络、遗传算法及混沌理论等新学科的融合，正在显示出其巨大的应用潜力。实质上模糊控制是一种非线性控制，从属于智能控制的范畴。模糊控制的一大特点是既具有系统化的理论，又有着大量实际应用背景。本文简单介绍了模糊控制的概念及应用，详细介绍了模糊控制器的设计，其中包含模糊控制系统的原理、模糊控制器的分类及其设

2、计元素。关键词：模糊控制；模糊控制器；现状及展望abstract fuzzy control theory is based on fuzzy mathematics, using language rule representation and advanced computer technology, it is a high-level control strategy which can make decision by the fuzzy reasoning. fuzzy control is a computer numerical contro which based fuzzy

3、 set theory, fuzzy linguistic variables and fuzzy logic, it has become the effective form of intelligent control especially in the form of fuzzy control and neural networks, genetic algorithms and chaos theory and other new integration of disciplines, which is showing its great potential. fuzzy cont

4、rol is essentially a nonlinear control, and subordinates intelligent control areas. a major feature of fuzzy control is both a systematic theory and a large number of the application background.this article introduces simply the concept and application of fuzzy control and introduces detailly the de

5、sign of the fuzzy controller. it contains the principles of fuzzy control system, the classification of fuzzy controller and its design elements.key words: fuzzy control; fuzzy controller; status and prospects.引言传统的常规pid控制方式是根据被控制对象的数学模型建立，虽然它的控制精度可以很高，但对于多变量且具有强耦合性的时变系统表现出很大的误差。比例调节是根据被调量和设定值之间的差值来

6、变化的，也就是说比例控制中余差不可避免。积分调节最终实现无余差调节，但是超调比较大。模糊控制是建立在人工经验基础之上的,它能将熟练操作员的实践经验加以总结和描述,并用语言表达出来,得到定性的、精确的控制规则,不需要被控对象的数学模型。并且模糊控制易于被人们接受,构造容易,适应性好。the introductiontraditional way of conventional pid control was established according to the mathematical model of controlled object, although it can be very h

7、igh control precision, but for the multi-variable30 and time-varying systems with strong coupling showed great error. proportional control is based on the difference in value between set value and quantity of the modulated to change, that is to say, proportional control of residual is inevitable. in

8、tegral regulation achieve everything in a glance at poor regulation, but the overshoot is bigger. fuzzy control is based on the artificial experience, it can skilled operator's practical experience summarized and described, and the language expression, get a qualitative, precise control rules, n

9、ot need mathematical model of controlled object. and fuzzy control is easy to be accepted by people, easy structure, good adaptability.第一章 模糊控制概述1.1模糊控制的概念及应用“模糊”是人类感知万物,获取知识,思维推理,决策实施的重要特征。“模糊”比“清晰”所拥有的信息容量更大,内涵更丰富,更符合客观世界。模糊逻辑控制(fuzzy logic control)简称模糊控制(fuzzy control)，是以模糊集合论、模糊语言变量和模糊逻辑推理为基础的一种

10、计算机数字控制技术。模糊控制理论是由美国著名的学者加利福尼亚大学教授zadeh·l·a于1965年首先提出,它是以模糊数学为基础,用语言规则表示方法和先进的计算机技术,由模糊推理进行决策的一种高级控制策。在19681973年期间zadeh·l·a先后提出语言变量、模糊条件语句和模糊算法等概念和方法，使得某些以往只能用自然语言的条件语句形式描述的手动控制规则可采用模糊条件语句形式来描述，从而使这些规则成为在计算机上可以实现的算法。1974年,英国伦敦大学教授mamdani·e·h研制成功第一个模糊控制器, 并把它应用于锅炉和蒸汽机的控制

11、，在实验室获得成功。这一开拓性的工作标志着模糊控制论的诞生并充分展示了模糊技术的应用前景。模糊控制实质上是一种非线性控制，从属于智能控制的范畴。模糊控制的一大特点是既具有系统化的理论，又有着大量实际应用背景。模糊控制的发展最初在西方遇到了较大的阻力；然而在东方尤其是在日本，却得到了迅速而广泛的推广应用。其典型应用的例子涉及生产和生活的许多方面， 以下为模糊控制在工业和生活方面的一些应用实例：1净水场药品注人控制、上下水道处理系统2各种溶沪： 电气炉 水泥生成炉的控翻、原子能发电供水控制、金属板成形控制3城市垃圾焚烧炉的控制4随道盾构机械、油压掘进机械、集装箱吊装的控制5高速公路隧道的排气、换气

12、控制6汽车的定速行走控制、发动机的控制、模糊at 、自动观光船7飞机离着陆控制、直升飞机控制、海上救难船控制8机器人的控制： 扫除机械人、花道机械人、激光切割机器人、钓鱼机器人、9升降机群管理、自动枪票机、自动门开关装置、自动贩卖机10空调控制制冷 、制热机、多路空调系统、铁道车辆、11造纸机、清酒酿造控制12自动声音调整器、传感器位置选择13电视会议系统、簇像机、电子喷水器、录像机、照像机、复印机、绘图机14家电制品: 洗衣机、吸尘器、干澡机、冷藏箱、电子微波炉,电饭锅、电动剃须刀、1.2模糊控制的优点 1简化系统设计的复杂性，特别适用于非线性、时变、模型不完全的系统上。 2利用控制法则来描

13、述系统变量间的关系。 3不用数值而用语言式的模糊变量来描述系统，模糊控制器不必对被控制对象建立完整的数学模式。 4模糊控制器是一语言控制器，使得操作人员易于使用自然语言进行人机对话。 5模糊控制器是一种容易控制、掌握的较理想的非线性控制器，并且抗干扰能力强，响应速度快，并对系统参数的变化有较强的鲁棒性和较佳的容错性。 6从属于智能控制的范畴。该系统尤其适于非线性，时变，滞后系统的控制。1.3模糊控制的缺点 1模糊控制的设计尚缺乏系统性，这对复杂系统的控制是难以奏效的。所以如何建立一套系统的模糊控制理论，以解决模糊控制的机理、稳定性分析、系统化设计方法等一系列问题； 2 如何获得模糊规则及隶属函

14、数即系统的设计办法，这在目前完全凭经验进行； 3 信息简单的模糊处理将导致系统的控制精度降低和动态品质变差。若要提高精度则必然增加量化级数，从而导致规则搜索范围扩大，降低决策速度，甚至不能实时控制； 4.如何保证模糊控制系统的稳定性即如何解决模糊控制中关于稳定性和鲁棒性问题还有待完善。the first chapter is summary of fuzzy control1.1 the concept and application of fuzzy control"fuzzy" human perception is everything, to acquire kno

15、wledge, thinking, reasoning, decision-making of important features. "fuzzy" than "clear" have the information capacity of a larger, more abundant connotation, more in line with the objective world. fuzzy logic control (fuzzy logic control) referred to as "fuzzy control (fuzz

16、y control), based on the fuzzy set theory, fuzzy language variable and fuzzy logic reasoning is the basis of a computer numerical control technology. fuzzy control theory is by the famous scholar at the university of california professor zadeh, l. a. first proposed in 1965, it is based on fuzzy math

17、ematics, expressed in the language rules method and advanced computer technology, by the fuzzy reasoning to make decisions of an advanced control strategy. during the period of 1968 1973, zadeh, l. a. successively proposed language variable, fuzzy algorithm and the fuzzy conditional statement concep

18、ts and methods, make some of the past can only use natural language form of conditional statements describe the manual control rules can be used to describe fuzzy conditional statement form, so as to make these rules can be implemented on computer algorithm. in 1974, a professor at the university of

19、 london mamdani, e, h, successfully developed the first fuzzy controller and apply it to the boiler and the control of the steam engine, to succeed in the laboratory. this pioneering work marks the birth of the fuzzy control theory and fully shows the application prospect of fuzzy technology.fuzzy c

20、ontrol is essentially a kind of nonlinear control, from belongs to the category of intelligent control. fuzzy control is one of the biggest characteristic is both a systematic theory, and with a large number of practical application background. the development of fuzzy control is first encountered i

21、n the west the larger resistance; in the east, especially in japan, however, has obtained the rapid and extensive popularization and application. its typical application example involves many aspects of production and life, the following is some applications of fuzzy control in the aspect of industr

22、ial and living example:1drug injection water purification field population control, sewage treatment system2all kinds of soluble shanghai: cement generated in electric furnace control turn, nuclear power generation, water supply, sheet metal forming control3control of urban garbage incinerator4the t

23、unnel shield machine, hydraulic excavating machinery, container lifting control5exhaust and ventilation control of highway tunnel6in constant speed control, automotive engine control, automatic fuzzy ats, sightseeing boats7aircraft, helicopters from landing control control, maritime rescue boat8the

24、robot's control: cleaning robots, ikebana robots, fishing, laser cutting machine,9lift the fleet management, gun ticket machine, automatic switching device, automatic vending machines10refrigeration and air conditioning control system of engine, multi-channel air conditioning system, railway veh

25、icle,11paper machine, wine brewing control12automatic sound regulator, sensor location choice13video conference system, cluster like machine, electronic sprinkler, vcr, camera, copy machine, drawing machine14home appliances products: washing machines, vacuum cleaners, dry bath machine, cooler, elect

26、ronic oven, electric rice cooker, electric razor1.2 the advantages of fuzzy control1 to simplify the complexity of system design, especially for nonlinear, time-varying and model incomplete system.2 control law is used to describe the relationship between the system variables.3 language instead of n

27、umerical type of fuzzy variables to describe the system, the fuzzy controller don't need to establish a comprehensive mathematical model of controlled object.4 controller, fuzzy controller is a language that operators easy to use natural language for the man-machine dialogue.5 fuzzy controller i

28、s a kind of easy to control and mastery of the ideal nonlinear controller, and strong anti-jamming capability, fast response speed, and the change of system parameters has strong robustness and better fault tolerance.6 from belongs to the category of intelligent control. this system is especially su

29、itable for nonlinear, time varying and lag control system.1.3 the disadvantage of fuzzy control1 the design of the fuzzy control is still lack of systematic, the control of complex systems is difficult to work. so how to establish a system of fuzzy control theory, in order to solve fuzzy control mec

30、hanism, stability analysis, systematic design method for a series of problems;2 how to obtain fuzzy rules and membership functions, system design, complete with experience for this in the present;3 simple fuzzy information processing will reduce control precision of the system and the dynamic qualit

31、y becomes poor. if you want to improve the accuracy of inevitable to increase the quantitative series, leading to rule search scope, reduce the decision-making speed, can't even real-time control;4. how to ensure the stability of the fuzzy control system is how to solve the fuzzy control on the

32、stability and robustness problems remains to be perfect.第二章 模糊控制器的设计2.1 模糊控制系统的原理模糊控制作为以模糊集合论、模糊语言变量及模糊逻辑推理为基础的一种计算机数字控制，它已成为目前实现智能控制的一种重要而又有效的形式尤其是模糊控制和神经网络、遗传算法及混沌理论等新学科的融合，正在显示出其巨大的应用潜力。控制规律执行机构 被控对象传感器给定量图1 常见负反馈控制系统方框图由测量装置、控制器、被控对象及执行机构组成的自动控制系统，就是人们所悉知的常规负反馈控制系统。其结构如图1所示。然而经过人们长期研究和实践形成的经典控制理

33、论，虽然对于解决线性定常系统的控制问题非常有效。随着计算机尤其是微机的发展和应用，基于由于式中模糊量，所以为了对被控对象施加精确的控制，还需要将其清晰化转换为精确量u，然后经d/a得模拟量送给执行机构，对被对象进行第一步控制。然后中断等待第二次采样，进行第二步控制.这样循环下去就实现了对被控对象的模糊控制。2.2模糊控制器的基本结构模糊控制器的基本结构包括知识库、模糊推理、输入量模糊化、输出量精确化四部分。1.知识库 知识库包括模糊控制器参数库和模糊控制规则库。模糊控制规则建立在语言变量的基础上。语言变量取值为“大”、“中”、“小”等这样的模糊子集,各模糊子集以隶属函数表明基本论域上的精确值属

34、于该模糊子集的程度。因此,为建立模糊控制规则,需要将基本论域上的精确值依据隶属函数归并到各模糊子集中,从而用语言变量值(大、中、小等)代替精确值。这个过程代表了人在控制过程中对观察到的变量和控制量的模糊划分。由于各变量取值范围各异,故首先将各基本论域分别以不同的对应关系,映射到一个标准化论域上。通常,对应关系取为量化因子。为便于处理,将标准论域等分离散化,然后对论域进行模糊划分,定义模糊子集,如nb、pz、ps等。 同一个模糊控制规则库,对基本论域的模糊划分不同,控制效果也不同。具体来说,对应关系、标准论域、模糊子集数以及各模糊子集的隶属函数都对控制效果有很大影响。这3类参数与模糊控制规则具有

35、同样的重要性,因此把它们归并为模糊控制器的参数库,与模糊控制规则库共同组成知识库。 2.模糊化 将精确的输入量转化为模糊量f有两种方法: (1)将精确量转换为标准论域上的模糊单点集。精确量x经对应关系g转换为标准论域x上的基本元素,则该元素的模糊单点集f为 uf(u)=1 if u=g(x) (2)将精确量转换为标准论域上的模糊子集。 精确量经对应关系转换为标准论域上的基本元素,在该元素上具有最大隶属度的模糊子集,即为该精确量对应的模糊子集。 3.模糊推理 最基本的模糊推理形式为: 前提1if a then b 前提2if a 结论then b 其中,a、a为论域u上的模糊子集,b、b为论域v

36、上的模糊子集。前提1称为模糊蕴涵关系,记为ab。在实际应用中,一般先针对各条规则进行推理,然后将各个推理结果总合而得到最终推理结果。 4.精确化 推理得到的模糊子集要转换为精确值,以得到最终控制量输出y。目前常用两种精确化方法: (1)最大隶属度法。在推理得到的模糊子集中,选取隶属度最大的标准论域元素的平均值作为精确化结果。 (2)重心法。将推理得到的模糊子集的隶属函数与横坐标所围面积的重心所对应的标准论域元素作为精确化结果。在得到推理结果精确值之后,还应按对应关系,得到最终控制量输出y。2.3模糊控制器的分类模糊控制的类型有:(1)基本模糊控制器：一旦模糊控制表确定之后,控制规则就固定不变了

37、;(2)自适应模糊控制器：在运行中自动修改、完善和调整规则,使被控过程的控制效果不断提高,达到预期的效果；(3)智能模糊控制器：它把人、人工智能和神经网络三者联系起来,实现综合信息处理,使系统既具有灵活的推理机制、启发性知识与产生式规则表示,又具有多种层次、多种类型的控制规律选择。2.4模糊控制器的设计模糊控制器在模糊自动控制系统中具有举足轻重的作用，因此在模糊控制系统中，设计和调整模糊控制器的工作是很重要的。模糊控制器的设计包括以下几项内容：1、确定模糊控制器的输入变量和输出变量；2、设计模糊控制规则，并计算模糊控制规则所决定的模糊关系，建立模糊控制表；3、确立模糊化和非模糊化方法；4、合理

38、选择模糊控制算法的采样时间。2.4.1模糊控制器的输入输出变量由于模糊控制器的控制规则是通过模拟人脑的思维决策方式提出的，所以在选择模糊控制器的输入输出变量时，必须深入研究人在手动控制过程中是如何获取和输出信息的。由于人在手动控制过程中，主要是根据误差、误差的变化及误差的变化的变化来实现控制的，所以模糊控制器的输入变量也可有三个，即误差、误差的变化及误差的变化的变化，输出变量一般选择控制量的变化。通常将模糊控制器输入变量的个数称为模糊控制的维数。由于一般情况下，一维模糊控制器的动态控制性能并不好，三维模糊控制器的控制规则过于复杂，控制算法的实现比较困难，所以，目前被广泛采用的均为二维模糊控制器

39、，这种控制器以误差和误差的变化为输入变量，以控制量的变化为输出变量。整个论域即在定义这些模糊子集时应注意使论域中任何一点对这些模糊子集的隶属度的最大值不能太小，否则会在这样的点附近出现不灵敏区，以至于造成失控，使模糊控制系统控制性能变坏。2.4.2建立模糊控制器的控制规则建立模糊控制规则的基本思想:当误差大或较大时，选择控制量以尽快消除误差为主，而当误差较小时，选择控制量要注意防止超调，以系统的稳定性为主要出发点。模糊控制规则的来源有3条途径:基于专家经验和实际操作,基于模糊模型,基于模糊控制的自学习。模糊控制器的控制规则作为人工手动控制策略的语言描述，它通常用条件语句表示。其主要形式可概括如

40、下： if a then b if a then b else c if a and b then c if a then if b then c if a or b and c or d then e if a then b and if a then c if a then b, c if a then b1 else if a2 then b2知道上述条件语句之后。以二维模糊控制器为例，假设条件语句形式为if e= a then if c= bj then u=cij(i=1,2,.,n;j=1,2 .,m)，式中ai bj cij分别定义在误差、误差变化和控制量论域x,y, z上的模糊

41、集;e, c, u分别代表误差、误差变化和控制模糊变量。2.4.3确立模糊化和精确化化方法一 模糊化方法由于计算机采样输入的变量均为精确量，所以为便于实现模糊控制算法，须经过模糊量化处理变为模糊量。模糊化一般采用如下两种方法：1、将在某区间的精确量x模糊化成这样的一个模糊子集，它在点x处隶属度为1，除x点外其余各点的隶属均取0。如所选模糊集合论域为x=-n,-n+1,.,0,.,n-l,n，而输入的基本论域为-e,e，输入精确量为e。2、首先同上算法得到l,其次查找语言变量赋值表，找出1位置上与最大隶属度所对应的语言值所决定的模糊量，该模糊量便为e的模糊化量。二 精确化方法在模糊控制系统中，由

42、于对建立的模糊控制规则通过模糊推理决策出的控制变量是一个模糊子集，它不能直接控制被控对象，所以还需要采取合理的方法将其转换为精确量，以便最好的发挥出模糊推理结果的决策效果。精确化过程的方法很多，主要有min-max重心法、代数积-加法-重心法、模糊加权型推理法、函数型推理法、加权函数型推理法、选择最大隶属度法、取中位数法。2.4.4采样时间的选择选择采样时间是计算机控制中的构性问题，所以模糊控制作为计算机控制的一种类型，也存在合理的选择采样时间的问题。香农采样定理给出了选择采样周期的下限.即 式中为采样信号的上限角频率。在此范围内，采样周期越小，就接近连续控制。但也不能太小，它需要综合考虑执行

43、机构响应时间、计算机控制算法所需时间、计算机字长、抗干扰性能等多方面因素the second chapter, the design of fuzzy controller2.1 the principle of fuzzy control systemfuzzy control as fuzzy set theory, fuzzy language variable and fuzzy logic reasoning on the basis of a computer numerical control, it has become the realization of intellige

44、nt control is an important and effective especially in the form of fuzzy control and neural network, genetic algorithm and the fusion of new disciplines such as chaos theory, is showing its great potential applications.a common negative feedback control system block diagram in figure 1by measuring d

45、evice, controller and controlled object and actuator of the automatic control system, is that people know know the regular feedback control system. its structure is shown in figure 1. yet after a long-term research and practice of classical control theory, although for solving the control problem of

46、 linear time-invariant system is very effective. along with the computer, especially the development and application of microcomputer based on the type of mu fuzzy quantity, so in order to exert precise control on the controlled, still need to convert their motivation to accurate quantity to u, and

47、then the d/a analog to actuators, for the first step in the control object. then stop waiting for the second sample, carries on the second step control. this loop is realized with fuzzy control of the controlled object.2.2 the basic structure of fuzzy controllerthe basic structure of fuzzy controlle

48、r includes knowledge base, fuzzy reasoning, fuzziness of input, output, high-precision four parts.1. the knowledge baselibrary knowledge base including fuzzy controller parameters and fuzzy control rule base. on the basis of fuzzy control rules based on linguistic variable. language state variable i

49、s the "big", ""," small ", such as the fuzzy subset, the fuzzy subset to subordinate function shows that the basic theory of precision value belongs to the fuzzy subset of the domain. therefore, in order to establish fuzzy control rules, requires the accurate values on

50、the basic theory of domain based on membership function are incorporated into the fuzzy subset, to use the language variable values (large, medium and small, etc.) instead of the accurate values. this process represents the people of observed variables in the control process and control the amount o

51、f fuzzy partition. due to the different variable scope, so the first will be the basic theory of domain respectively in different corresponding relations, mapped to a standardized theory field. usually, the corresponding relationship between off for quantitative factors. for ease of handling, biaozh

52、unlun domain such as bulk chemical separation, and then to fuzzy partition of discourse, define the fuzzy subset, such as nb, pz, ps, etc.the same fuzzy control rule base, fuzzy partition to the fundamental theory of domain is different, the control effect is also different. specifically, correspond

53、ence, biaozhunlun domain, the number of fuzzy subset, and the membership function of fuzzy subset has a great influence on the control effect. these three kinds of parameters and the fuzzy control rules have the same importance, therefore to merge them into fuzzy controller parameter database, toget

54、her with the fuzzy control rule base of the knowledge base.2. the blurconvert accurate input into fuzzy quantity f there are two ways:(1) converts gauged biaozhunlun fuzzy single point sets on the domain. gauged by the corresponding relation between x x g into biaozhunlun domain on the basic element

55、s, then the elements of the fuzzy single point set fuf (u) = 1 if u = g (x)(2) converts gauged biaozhunlun domain of fuzzy subsets.gauged by the corresponding relationship into biaozhunlun domain on the basic elements, on the element has the maximum membership degree of fuzzy subsets, namely for the

56、 precise amount corresponding fuzzy subset.3. the fuzzy inferencethe most basic form of fuzzy reasoning is:1 if a then bpremise 2 if a 'conclusion then b 'among them, a, a 'for fuzzy subset on the theory of domain u, b, b' for the theory of fuzzy subset v on the domain. premise 1 is

57、called the fuzzy implication relations, to a and b. in practice, the general rules of first in view of the individual reasoning, then the reasoning result sum and eventually reasoning results are obtained.4. accuratereasoning of fuzzy subset to convert accurate value, to get the final control output y. two accurate methods commonly used at present:(1) the maximum membership degree method. in the reasoning of fuzzy subset, the selection membership degree of the largest biaozhunlun domain element as the avera