智能控制課件

智能控制智能控制概述

智能控制是自動(dòng)控制發(fā)展的一個(gè)新階段，是人工智慧、控制論、系統(tǒng)論和資訊理論等多種學(xué)科的綜合與集成，是當(dāng)前的一個(gè)研究熱點(diǎn)。

智能控制的基本概念智能控制系統(tǒng)的特點(diǎn)智能控制系統(tǒng)的結(jié)構(gòu)理論智能控制與傳統(tǒng)控制的關(guān)係智能控制的研究對(duì)象智能控制的類型智能控制的發(fā)展概述智能控制的基本概念

智能控制已經(jīng)出現(xiàn)了相當(dāng)長(zhǎng)的一段時(shí)間，並且已取得了初步的應(yīng)用成果.但是究竟什麼是“智能”，什麼是“智能控制”等問題，至今仍沒有統(tǒng)一的定義。歸納起來，主要有如下四種說法：

智能控制的基本概念智能控制的定義一:智能控制是由智能機(jī)器自主地實(shí)現(xiàn)其目標(biāo)的過程。而智能機(jī)器則定義為，在結(jié)構(gòu)化或非結(jié)構(gòu)化的、熟悉的或陌生的環(huán)境中，自主地或與人交互地執(zhí)行人類規(guī)定的任務(wù)的一種機(jī)器。

定義二:K.J.奧斯托羅姆則認(rèn)為，把人類具有的直覺推理和試湊法等智能加以形式化或機(jī)器模擬，並用於控制系統(tǒng)的分析與設(shè)計(jì)中，以期在一定程度上實(shí)現(xiàn)控制系統(tǒng)的智能化，這就是智能控制。他還認(rèn)為自調(diào)節(jié)控制、自適應(yīng)控制就是智能控制的低級(jí)體現(xiàn)。智能控制的基本概念定義三:智能控制是一類無需人的干預(yù)就能夠自主地驅(qū)動(dòng)智能機(jī)器實(shí)現(xiàn)其目標(biāo)的自動(dòng)控制，也是用電腦模擬人類智能的一個(gè)重要領(lǐng)域。智能控制的基本概念定義四:

智能控制實(shí)際只是研究與模擬人類智能活動(dòng)及其控制與資訊傳遞過程的規(guī)律，研製具有仿人智能的工程控制與資訊處理系統(tǒng)的一個(gè)新興分支學(xué)科。智能控制的基本概念智能控制的特點(diǎn)同時(shí)具有以知識(shí)表示的非數(shù)學(xué)廣義模型和以數(shù)學(xué)模型表示的混合過程,，也往往是那些含有複雜性、不完全性、模糊性或不確定性以及不存在已知演算法的非數(shù)學(xué)過程，並以知識(shí)進(jìn)行推理，以啟發(fā)引導(dǎo)求解過程;智能控制的核心在高層控制，即組織級(jí);

智能控制器具有非線性特性;智能控制具有變結(jié)構(gòu)特點(diǎn);智能控制器具有總體自尋優(yōu)特性;智能控制系統(tǒng)應(yīng)能滿足多樣性目標(biāo)的高性能要求;智能控制是一門邊緣交叉學(xué)科;智能控制是一個(gè)新興的研究領(lǐng)域.智能控制的特點(diǎn)

智能控制的結(jié)構(gòu)理論

智能控制的理論結(jié)構(gòu)明顯地具有多學(xué)科交叉的特點(diǎn)，許多研究人員試圖建立起智能控制這一新學(xué)科，他們提出了一些有關(guān)智能控制系統(tǒng)結(jié)構(gòu)的思想。按照（傅京孫）和Saridis提出的觀點(diǎn)，可以把智能控制看作是人工智慧、自動(dòng)控制和運(yùn)籌學(xué)三個(gè)主要學(xué)科相結(jié)合的產(chǎn)物。稱之為三元結(jié)構(gòu)。

智能控制的結(jié)構(gòu)理論IC=AI∩AC∩ORIC─

智能控制(IntelligentControl);OR─運(yùn)籌學(xué)(OperationResearch）Al─人工智慧(ArtificialIntelligence);AC一自動(dòng)控制(AutomaticControl);∩一表示交集.智能控制的結(jié)構(gòu)理論人工智慧（AI）：是一個(gè)知識(shí)處理系統(tǒng)，具有記憶、學(xué)習(xí)、資訊處理、形式語言、啟發(fā)式推理等功能。自動(dòng)控制（AC）：描述系統(tǒng)的動(dòng)力學(xué)特性，是一種動(dòng)態(tài)回饋。運(yùn)籌學(xué)（OR）：是一種定量?jī)?yōu)化方法，如線性規(guī)劃、網(wǎng)路規(guī)劃、調(diào)度、管理、優(yōu)化決策和多目標(biāo)優(yōu)化方法等。智能控制的結(jié)構(gòu)理論

智能控制就是應(yīng)用人工智慧的理論與技術(shù)和運(yùn)籌學(xué)的優(yōu)化方法，並將其同控制理論方法與技術(shù)相結(jié)合，在未知環(huán)境下，仿效人的智能，實(shí)現(xiàn)對(duì)系統(tǒng)的控制?？梢?，智能控制代表著自動(dòng)控制學(xué)科發(fā)展的最新進(jìn)程。智能控制的結(jié)構(gòu)理論

智能控制與傳統(tǒng)控制的關(guān)係傳統(tǒng)控制（Conventionalcontrol）：經(jīng)典回饋控制和現(xiàn)代理論控制。它們的主要特徵是基於精確的系統(tǒng)數(shù)學(xué)模型的控制。適於解決線性、時(shí)不變等相對(duì)簡(jiǎn)單的控制問題。智能控制（Intelligentcontrol）以上問題用智能的方法同樣可以解決。智能控制是對(duì)傳統(tǒng)控制理論的發(fā)展，傳統(tǒng)控制是智能控制的一個(gè)組成部分，在這個(gè)意義下，兩者可以統(tǒng)一在智能控制的框架下。智能控制的研究對(duì)象智能控制主要應(yīng)用在以下情況：實(shí)際系統(tǒng)由於存在複雜性、非線性、時(shí)變性、不確定性和不完全性等，一般無法獲得精確的數(shù)學(xué)模型。應(yīng)用傳統(tǒng)控制理論進(jìn)行控制必須提出並遵循一些比較苛刻的線性化假設(shè)，而這些假設(shè)在應(yīng)用中往往與實(shí)際情況不相吻合。智能控制的研究對(duì)象對(duì)於某些複雜的和飽含不確定性的控制過程，根本無法用傳統(tǒng)數(shù)學(xué)模型來表示，即無法解決建模問題。為了提高控制性能，傳統(tǒng)控制系統(tǒng)可能變得很複雜，從而增加了設(shè)備的投資，減低了系統(tǒng)的可靠性。智能控制的類型智能控制系統(tǒng)一般包括分級(jí)遞階控制系統(tǒng)專家控制系統(tǒng)神經(jīng)控制系統(tǒng)模糊控制系統(tǒng)學(xué)習(xí)控制系統(tǒng)集成或者（複合）混合控制：幾種方法和機(jī)制往往結(jié)合在一起，用於一個(gè)實(shí)際的智能控制系統(tǒng)或裝置，從而建立起混合或集成的智能控制系統(tǒng)。分級(jí)遞階控制系統(tǒng)分級(jí)遞階智能控制是在自適應(yīng)控制和自組織控制基礎(chǔ)上，由美國(guó)普渡大學(xué)Saridis提出的智能控制理論。分級(jí)遞階智能控制(HierarchicalIntelligentControl)主要由三個(gè)控制級(jí)組成，按智能控制的高低分為組織級(jí)、協(xié)調(diào)級(jí)、執(zhí)行級(jí)，並且這三級(jí)遵循“伴隨智能遞降精度遞增”原則，其功能結(jié)構(gòu)如下圖所示。分級(jí)遞階控制系統(tǒng)分級(jí)遞階控制系統(tǒng)組織級(jí)(organizationlevel)：組織級(jí)通過人機(jī)介面和用戶(操作員)進(jìn)行交互，執(zhí)行最高決策的控制功能，監(jiān)視並指導(dǎo)協(xié)調(diào)級(jí)和執(zhí)行級(jí)的所有行為，其智能程度最高。協(xié)調(diào)級(jí)(Coordinationlevel)：協(xié)調(diào)級(jí)可進(jìn)一步劃分為兩個(gè)分層:控制管理分層和控制監(jiān)督分層。執(zhí)行級(jí)(executivelevel)：執(zhí)行級(jí)的控制過程通常是執(zhí)行一個(gè)確定的動(dòng)作。專家控制系統(tǒng)（ExpertSystem）專家指的是那些對(duì)解決專門問題非常熟悉的人們，他們的這種專門技術(shù)通常源於豐富的經(jīng)驗(yàn)，以及他們處理問題的詳細(xì)專業(yè)知識(shí)。專家系統(tǒng)主要指的是一個(gè)智能電腦程式系統(tǒng)，其內(nèi)部含有大量的某個(gè)領(lǐng)域?qū)＜宜疁?zhǔn)的知識(shí)與經(jīng)驗(yàn)，能夠利用人類專家的知識(shí)和解決問題的經(jīng)驗(yàn)方法來處理該領(lǐng)域的高水準(zhǔn)難題.它具有啟發(fā)性、透明性、靈活性、符號(hào)操作、不一確定性推理等特點(diǎn)。應(yīng)用專家系統(tǒng)的概念和技術(shù)，模擬人類專家的控制知識(shí)與經(jīng)驗(yàn)而建造的控制系統(tǒng)，稱為專家控制系統(tǒng)。

神經(jīng)網(wǎng)路是指由大量與生物神經(jīng)系統(tǒng)的神經(jīng)細(xì)胞相類似的人工神經(jīng)元互連而組成的網(wǎng)路；或由大量象生物神經(jīng)元的處理單元並聯(lián)互連而成。這種神經(jīng)網(wǎng)路具有某些智能和仿人控制功能。

人工神經(jīng)網(wǎng)路控制系統(tǒng)

學(xué)習(xí)演算法是神經(jīng)網(wǎng)路的主要特徵，也是當(dāng)前研究的主要課題.學(xué)習(xí)的概念來自生物模型，它是機(jī)體在複雜多變的環(huán)境中進(jìn)行有效的自我調(diào)節(jié)。神經(jīng)網(wǎng)路具備類似人類的學(xué)習(xí)功能。一個(gè)神經(jīng)網(wǎng)路若想改變其輸出值，但又不能改變它的轉(zhuǎn)換函數(shù)，只能改變其輸人，而改變輸人的唯一方法只能修改加在輸人端的加權(quán)係數(shù)。人工神經(jīng)網(wǎng)路控制系統(tǒng)

人工神經(jīng)網(wǎng)路控制系統(tǒng)神經(jīng)網(wǎng)路的學(xué)習(xí)過程是修改加權(quán)係數(shù)的過程，最終使其輸出達(dá)到期望值，學(xué)習(xí)結(jié)束.常用的學(xué)習(xí)演算法有：Hebb學(xué)習(xí)演算法、widrow-Hoff學(xué)習(xí)演算法、反向傳播學(xué)習(xí)演算法一BP學(xué)習(xí)演算法、Hopfield回饋神經(jīng)網(wǎng)路學(xué)習(xí)演算法等。模糊控制系統(tǒng)所謂模糊控制，就是在被控制對(duì)象的模糊模型的基礎(chǔ)上，運(yùn)用模糊控制器近似推理手段，實(shí)現(xiàn)系統(tǒng)控制的一種方法。模糊模型是用模糊語言和規(guī)則描述的一個(gè)系統(tǒng)的動(dòng)態(tài)特性及性能指標(biāo)。模糊控制系統(tǒng)模糊控制的基本思想是用機(jī)器去模擬人對(duì)系統(tǒng)的控制。它是受這樣事實(shí)而啟發(fā)的：對(duì)於用傳統(tǒng)控制理論無法進(jìn)行分析和控制的複雜的和無法建立數(shù)學(xué)模型的系統(tǒng)，有經(jīng)驗(yàn)的操作者或?qū)＜覅s能取得比較好的控制效果，這是因?yàn)樗麄儞碛腥辗e月累的豐富經(jīng)驗(yàn)，因此人們希望把這種經(jīng)驗(yàn)指導(dǎo)下的行為過程總結(jié)成一些規(guī)則，並根據(jù)這些規(guī)則設(shè)計(jì)出控制器。然後運(yùn)用模糊理論、模糊語言變數(shù)和模糊邏輯推理的知識(shí)，把這些模糊的語言上升為數(shù)值運(yùn)算，從而能夠利用電腦來完成對(duì)這些規(guī)則的具體實(shí)現(xiàn)，達(dá)到以機(jī)器代替人對(duì)某些對(duì)象進(jìn)行自動(dòng)控制的目的。學(xué)習(xí)控制系統(tǒng)

學(xué)習(xí)是人類的主要智能之一，人類的各項(xiàng)活動(dòng)也需要學(xué)習(xí)。在人類的進(jìn)化過程中，學(xué)習(xí)功能起著十分重要的作用。學(xué)習(xí)控制正是模擬人類自身各種優(yōu)良的控制調(diào)節(jié)機(jī)制的一種嘗試。所謂學(xué)習(xí)是一種過程，它通過重複輸人信號(hào)，並從外部校正該系統(tǒng)，從而使系統(tǒng)對(duì)特定輸人具有特定回應(yīng)。學(xué)習(xí)控制系統(tǒng)是一個(gè)能在其運(yùn)行過程中逐步獲得受控過程及環(huán)境的非預(yù)知資訊，積累控制經(jīng)驗(yàn)，並在一定的評(píng)價(jià)標(biāo)準(zhǔn)下進(jìn)行估值、分類、決策和不斷改善系統(tǒng)品質(zhì)的自動(dòng)控制系統(tǒng)。

智能控制的發(fā)展趨勢(shì)智能控制是自動(dòng)控制理論發(fā)展的必然趨勢(shì)

人工智慧為智能控制的產(chǎn)生提高了機(jī)遇智能控制的發(fā)展概述

自動(dòng)控制理論是人類在征服自然、改造自然的鬥爭(zhēng)中形成和發(fā)展的。控制理論從形成發(fā)展至今，已經(jīng)經(jīng)歷多年的歷程，分為三個(gè)階段。第一階段是以上世紀(jì)40年代興起的調(diào)節(jié)原理為標(biāo)誌，稱為經(jīng)典控制理論階段；第二階段以60年代興起的狀態(tài)空間法為標(biāo)誌，稱為現(xiàn)代控制理論階段；第三階段則是80年代興起的智能控制理論階段。

智能控制的發(fā)展概述

傅京孫在1971年指出，方瞭解決智能控制的問題，用嚴(yán)格的數(shù)學(xué)方法研究發(fā)展新的工具，對(duì)複雜的"環(huán)境-對(duì)象"進(jìn)行建模和識(shí)別，以實(shí)現(xiàn)最優(yōu)控制，或者用人工智能的啟發(fā)式思想建立對(duì)不能精確定義的環(huán)境和任務(wù)的控制設(shè)計(jì)方法。這兩者都值得一試，而更重要的也許還是把這兩種途徑緊密地結(jié)合起來，協(xié)調(diào)地進(jìn)行研究。也就是說，對(duì)於複雜的環(huán)境和複雜的任務(wù)，如何，將人工智慧技術(shù)中較少依賴模型的問題的求解方法與常規(guī)的控制方法相結(jié)合，這正是智能控制所要解決的問題。

智能控制的發(fā)展概述

Saridis在學(xué)習(xí)控制系統(tǒng)研究的基礎(chǔ)上，提出了分級(jí)遞階和智能控制結(jié)構(gòu)，整個(gè)結(jié)構(gòu)自上而下分為組織級(jí)、協(xié)調(diào)級(jí)和執(zhí)行級(jí)三個(gè)層次，其中執(zhí)行級(jí)是面向設(shè)備參數(shù)的基礎(chǔ)自動(dòng)化級(jí)，在這一級(jí)不存在結(jié)構(gòu)性的不確定性，可以用常規(guī)控制理論的方法設(shè)計(jì)。協(xié)調(diào)級(jí)實(shí)際上是一個(gè)離散事件動(dòng)態(tài)系統(tǒng)，主要運(yùn)用運(yùn)籌學(xué)的方法研究。組織級(jí)涉及感知環(huán)境和追求目標(biāo)的高層決策等類似於人類智能的功能，可以借鑒人工智慧的方法來研究。因此，Saridis將傅京孫關(guān)於智能控制是人工智慧與自動(dòng)控制相結(jié)合的提法發(fā)展為:智能控制是人工智能、運(yùn)籌學(xué)和控制系統(tǒng)理論三者的結(jié)合。

智能控制的發(fā)展概述

1985年8月，IHE在美國(guó)紐約召開了第一屆智能控制學(xué)術(shù)討論會(huì)，智能控制原理和智能控制系統(tǒng)的結(jié)構(gòu)這一提法成為這次會(huì)議的主要議題。這次會(huì)議決定，在IEEE控制系統(tǒng)學(xué)會(huì)下設(shè)立一個(gè)IEEE智能控制專業(yè)委員會(huì)。這標(biāo)誌著智能控制這一新興學(xué)科研究領(lǐng)域的正式誕生。智能控制作為一門獨(dú)立的學(xué)科，己正式在國(guó)際上建立起來。智能技術(shù)在國(guó)內(nèi)也受到廣泛重視，中國(guó)自動(dòng)化學(xué)會(huì)等於1993年8月在北京召開了第一屆全球華人智能控制與智能自動(dòng)化大會(huì)，1995年8月在天津召開了智能自動(dòng)化專業(yè)委員會(huì)成立大會(huì)及首屆中國(guó)智能自動(dòng)化學(xué)術(shù)會(huì)議，1997年6月在西安召開了第二屆全球華人智能控制與智能自動(dòng)化大會(huì)。智能控制的發(fā)展概述

近年來，智能控制技術(shù)在國(guó)內(nèi)外已有了較大的發(fā)展，己進(jìn)入工程化、實(shí)用化的階段。但作為一門新興的理論技術(shù)，它還處在一個(gè)發(fā)展時(shí)期。然而，隨著人工智慧技術(shù)、電腦技術(shù)的迅速發(fā)展，智能控制必將迎來它的發(fā)展新時(shí)期。

Chapter2FuzzyControlFuzzyControl:TheBasicsOverviewFuzzyControl:ATutorialIntroductionGeneralFuzzySystemSimpleDesignExample:TheInvertedPendulumSimulationofFuzzyControlSystemQuestionsWhydoweselectfuzzycontrollersinmanyreal-worldsystems?Howmuchofthesuccesscanbeattributedtotheuseofthemathematicalmodelandconventionalcontroldesignapproach?Howmuchshouldbeattributedtothecleverheuristictuningthatthecontrolengineerusesuponimplementation?Ifweexploittheuseofheuristicinformationthroughouttheentiredesignprocess,canweobtainhigherperformancecontrolsystems?Howdowedesignafuzzycontroller?QuestionsWhetherdoweneedamodelinfuzzycontrol?Whataretheperformanceevaluationoffuzzycontrol?Whatshouldwepayattentioninfuzzycontrollerdesign?2.1OverviewWhatisthemotivationforturningtofuzzycontrol?Basically,thedifficulttaskofmodelingandsimulatingcomplexrealworldsystemsforcontrolsystemsdevelopment,especiallywhenimplementationissuesareconsidered,iswelldocumented.Evenifarelativelyaccuratemodelofadynamicsystemcanbedeveloped,itisoftentoocomplextouseincontrollerdevelopment,especiallyformanyconventionalcontroldesignproceduresthatrequirerestrictiveassumptionsfortheplant(e.g.,linearity).Itisforthisreasonthatinpracticeconventionalcontrollersareoftendevelopedviasimplemodelsoftheplantbehaviorthatsatisfythenecessaryassumptions,andviatheadhoc(special)tuningofrelativelysimplelinearornonlinearcontrollers.Regardless,itiswellunderstood(althoughsometimesforgotten)thatheuristicsentertheconventionalcontroldesignprocessaslongasyouareconcernedwiththeactualimplementationofthecontrolsystem.Itmustbeacknowledged,moreover,thatconventionalcontrolengineeringapproachesthatuseappropriateheuristicstotunethedesignhavebeenrelativelysuccessful.Youmayaskthefollowingquestions:Howmuchofthesuccesscanbeattributedtotheuseofthemathematicalmodelandconventionalcontroldesignapproach,andhowmuchshouldbeattributedtothecleverheuristictuningthatthecontrolengineerusesuponimplementation?Andifweexploittheuseofheuristicinformationthroughouttheentiredesignprocess,canweobtainhigherperformancecontrolsystems?Fuzzycontrolprovidesaformalmethodologyforrepresenting,manipulating,andimplementingahuman'sheuristicknowledgeabouthowtocontrolasystem.

Inthissectionweseektoprovideaphilosophyofhowtoapproachthedesignoffuzzycontrollers.ThefuzzycontrollerblockdiagramisgiveninFigure2.1,whereweshowafuzzycontrollerembeddedinaclosed-loopcontrolsystem.Theplantoutputsaredenotedbyy(t),itsinputsaredenotedbyu(t),andthereferenceinputtothefuzzycontrollerisdenotedbyr(t).Figure2.1Fuzzycontrollerarchitecture.Basically,youshouldviewthefuzzycontrollerasanartificialdecisionmakerthatoperatesinaclosed-loopsysteminrealtime.Itgathersplantoutputdatay(t),comparesittothereferenceinputr(t),andthendecideswhattheplantinputu(t)shouldbetoensurethattheperformanceobjectives,willbemet.Thefuzzycontrollerhasfourmaincomponents:(1)The"rule-base"holdstheknowledge,intheformofasetofrules,ofhowbesttocontrolthesystem.(2)Theinferencemechanismevaluateswhichcontrolrulesarerelevantatthecurrenttimeandthendecideswhattheinputtotheplantshouldbe.(3)Thefuzzificationinterfacesimplymodifiestheinputssothattheycanbeinterpretedandcomparedtotherulesintherule-base.(4)thedefuzzificationinterfaceconvertstheconclusionsreachedbytheinferencemechanismintotheinputstotheplant.Howdowedesignafuzzycontroller?Todesignthefuzzycontroller,thecontrolengineermustgatherinformationonhowtheartificialdecisionmakershouldactintheclosed-loopsystem.Sometimesthisinformationcancomefromahumandecisionmakerwhoperformsthecontroltask,whileatothertimesthecontrolengineercancometounderstandtheplantdynamicsandwritedownasetofrulesabouthowtocontrolthesystemwithoutoutsidehelp.These"rules"basicallysay,"Iftheplantoutputandreferenceinputarebehavinginacertainmanner,thentheplantinputshouldbesomevalue."Awholesetofsuch"If-Then"rulesisloadedintotherule-base,andaninferencestrategyischosen,thenthesystemisreadytobetestedtoseeiftheclosed-loopspecificationsaremet.

Thisbriefdescriptionprovidesaveryhighleveloverviewofhowtodesignafuzzycontrolsystem.Belowwewillexpandonthesebasicideasandprovidemoredetailsonthisprocedureanditsrelationshiptotheconventionalcontroldesignprocedure.ModelingIssuesandPerformanceObjectives

FuzzyControllerDesign

PerformanceEvaluationApplicationAreas2.1.1ModelingIssuesandPerformanceObjectives

Whether

doweneedamodelinfuzzycontrol?Peopleworkinginfuzzycontroloftensaythat"amodelisnotneededtodevelopafuzzycontroller,andthisisthemainadvantageoftheapproach."However,willaproperunderstandingoftheplantdynamicsbeobtainedwithouttryingtousefirstprinciplesofphysicstodevelopamathematicalmodel?Andwillaproperunderstandingofhowtocontroltheplantbeobtainedwithoutsimulation-basedevaluationsthatalsoneedamodel?Wealwaysknowroughlywhatprocesswearecontrolling(e.g,weknowwhetheritisavehicleoranuclearreactor),anditisoftenpossibletoproduceatleastanapproximatemodel,sowhynotdothis?Forasafety-criticalapplication,ifyoudonotuseaformalmodel,thenitisnotpossibletoperformmathematicalanalysisorsimulation-basedevaluations.Isitwisetoignoretheseanalyticalapproachesforsuchapplications?Clearly,therewillbesomeapplicationswhereyoucansimply"hack"togetheracontroller(fuzzyorconventional)andgodirectlytoimplementation.Insuchasituationthereisnoneedforaformalmodeloftheprocess;however,isthistypeofcontrolproblemreallysochallengingthatfuzzycontrolisevenneeded?Couldaconventionalapproach(suchasPIDcontrol)ora"tablelook-up"schemeworkjustaswellorbetter,especiallyconsideringimplementationcomplexity?Overall,whenyoucarefullyconsiderthepossibilityofignoringtheinformationthatisfrequentlyavailableinamathematicalmodel,itisclearthatitwilloftenbeunwisetodoso.Basically,then,theroleofmodelinginfuzzycontroldesignisquitesimilartoitsroleinconventionalcontrolsystemdesign.Infuzzycontrolthereisamoresignificantemphasisontheuseofheuristics,butinmanycontrolapproaches(e.g,PIDcontrolforprocesscontrol)thereisasimilaremphasis.

Basically,infuzzycontrolthereisafocusontheuseofrulestorepresenthowtocontroltheplantratherthanordinarydifferentialequations(ODE).Thisapproachcanoffersomeadvantagesinthattherepresentationofknowledgeinrulesseemsmorelucidandnaturaltosomepeople.Forothers,though,theuseofdifferentialequationsismoreclearandnatural.Basically,thereissimplya"languagedifference"betweenfuzzyandconventionalcontrol:ODEsarethelanguageofconventionalcontrol,andrulesarethelanguageoffuzzycontrol.Theperformanceobjectivesanddesignconstraintsarethesameastheonesforconventionalcontrolthatwesummarizedabove,sincewestillwanttomeetthesametypesofclosed-loopspecifications.Thefundamentallimitationsthattheplantprovidesaffectourabilitytoachievehigh-performancecontrol,andthesearestillpresentjustastheywereforconventionalcontrol(e.g,nonminimumphaseorunstablebehaviorstillpresentschallengesforfuzzycontrol).

2.1.2FuzzyControllerDesign

Howdowedesignafuzzycontroller?Fuzzycontrolsystemdesignessentiallyamountsto(1)choosingthefuzzycontrollerinputsandoutputs,(2)choosingthepreprocessingthatisneededforthecontrollerinputsandpossiblypostprocessingthatisneededfortheoutputs,and(3)designingeachofthefourcomponentsofthefuzzycontrollershowninFigure1.2.

Asyouwillseeinthenextchapter,therearestandardchoicesforthefuzzificationanddefuzzificationinterfaces.Moreover,mostoftenthedesignersettlesonaninferencemechanismandmayusethisformanydifferentprocesses.Hence,themainpartofthefuzzycontrollerthatwefocusonfordesigntotherule-base.Whatisthemaintaskinfuzzycontrollerdesignprocess?Therule-baseisconstructedsothatitrepresentsahumanexpert"in-the-loop."Hence,theinformationthatweloadintotherulesintherule-basemaycomefromanactualhumanexpertwhohasspentalongtimelearninghowbesttocontroltheprocess.Inothersituationsthereisnosuchhumanexpert,andthecontrolengineerwillsimplystudytheplantdynamics(perhapsusingmodelingandsimulation)andwritedownasetofcontrolrulesthatmakessense.

Itisclearthatanyonewhohasexperiencedrivingacarcanpracticeregulatingthespeedaboutadesiredset-pointandloadthisinformationintoarule-base.Forinstance,onerulethatahumandrivermayuseis"Ifthespeedislowerthantheset-point,thenpressdownfurtherontheacceleratorpedal."Arulethatwouldrepresentevenmoredetailedinformationabouthowtoregulatethespeedwouldbe"Ifthespeedislowerthantheset-pointANDthespeedisapproachingtheset-pointveryfast,thenreleasetheacceleratorpedalbyasmallamount."Thissecondrulecharacterizesourknowledgeabouthowtomakesurethatwedonotover-shootourdesiredgoal(theset-pointspeed).Generallyspeaking,ifweloadverydetailedexpertiseintotherule-base,weenhanceourchancesofobtainingbetterperformance.2.1.3PerformanceEvaluation

Whataretheperformanceevaluationoffuzzycontrol?Eachandeveryideaonperformanceevaluationforconventionalcontrollersapplieshereaswell.Thebasicreasonforthisisthatafuzzycontrollerisanon-linearcontroller—somanyconventionalmodeling,analysis(viamathematics,simulation,orexperimentation),anddesignideasapplydirectly.Sincefuzzycontrolisarelativelynewtechnology,itisoftenquiteimportanttodeterminewhatvalueithasrelativetoconventionalmethods.Unfortunately,fewhaveperformeddetailedcomparativeanalysesbetweenconventionalandintelligentcontrolthathavetakenintoaccountawidearrayofavailableconventionalmethods(linear,nonlinear,adaptive,etc.);fuzzycontrolmethods(direct,adaptive,supervisory);theoretical,simulation,andexperimentalanalyses;computationalissues;andsoon.Whatshouldwepayattentioninfuzzycontrollerdesign?Moreover,mostworkinfuzzycontroltodatehasfocusedonlyonitsadvantagesandhasnottakenacriticallookatwhatpossibledisadvantages

therecouldbetousingit(hencethereadershouldbecautionedaboutthiswhenreadingtheliterature).Forexample,thefollowingquestionsarecauseforconcernwhenyouemployastrategyofgatheringheuristiccontrolknowledge:?Willthebehaviorsthatareobservedbyahumanexpertandusedtoconstructthefuzzycontrollerincludeallsituationsthatcanoccurduetodisturbances,noise,orplantparametervariations??Canthehumanexpertrealisticallyandreliablyforeseeproblemsthatcouldarisefromclosed-loopsysteminstabilitiesorlimitcycles??Willthehumanexpertbeabletoeffectivelyincorporatestabilitycriteriaandperformanceobjectives(e.g.,rise-time,overshoot,andtrackingspecifications)intoarule-basetoen-surethatreliableoperationcanbeobtained?Thesequestionsmayseemevenmoretroublesome(1)ifthecontrolprobleminvolvesasafety-criticalenvironmentwherethefailureofthecontrolsystemtomeetperformanceobjectivescouldleadtolossofhumanlifeoranenvironmentaldisaster,or(2)ifthehumanexpert'sknowledgeimplementedinthefuzzycontrollerissomewhatinferiortothatoftheveryexperiencedspecialistwewouldexpecttodesignthecontrolsystem(differentdesignershavedifferentlevelsofexpertise).Clearly,then,forsomeapplicationsthereisaneedforamethodologytodevelop,implement,andevaluatefuzzycontrollerstoensurethattheyarereliableinmeetingtheirperformancespecifications.Thisisthebasicthemeandfocusofthisbook.Fuzzysystemshavebeenusedinawidevarietyofapplicationsinengineering,science,business,medicine,psychology,andotherfields.Forinstance,inengineeringsomepotentialapplicationareasincludethefollowing:?

Aircraft/spacecraft:Flightcontrol,enginecontrol,avionicsystems,failurediagnosis,navigation,andsatelliteattitudecontrol.?

Automatedhighwaysystems:Automaticsteering,braking,andthrottlecontrolforvehicles.?

Automobiles:Brakes,transmission,suspension,andenginecontrol.?

Autonomousvehicles:Groundandunderwater.2.1.4ApplicationAreas?

Manufacturingsystems:Schedulinganddepositionprocesscontrol.?

Powerindustry:Motorcontrol,powercontrol/distribution,andloadestimation.?

Processcontrol:Temperature,pressure,andlevelcontrol,failurediagnosis,distillationcolumncontrol,anddesalinationprocesses.?

Robotics:Positioncontrolandpathplanning.Thislistisonlyrepresentativeoftherangeofpossibleapplicationsforthemethodsofthisbook.Othershavealreadybeenstudied,whilestillothersareyettobeidentified.Theprimarygoalofcontrolengineeringistodistillandapplyknowledgeabouthowtocontrolaprocesssothattheresultingcontrolsystemwillreliablyandsafelyachievehigh-performanceoperation.Inthischapterweshowhowfuzzylogicprovidesamethodologyforrepresentingandimplementingourknowledgeabouthowbesttocontrolaprocess.2.2FuzzyControl:ATutorialIntroduction

QuestionsHowdowechoosefuzzycontrollerinputsandoutputs?Howdoweputcontrolknowledgeintorule-bases?Howdowequantifytheknowledgeinfuzzycontrollerdesign?Whydothat?Whatareafuzzycontrollercomposed?AblockdiagramofafuzzycontrolsystemisshowninFigure2.1.Thefuzzycontrolleriscomposedofthefollowingfourelements:Figure2.2FuzzyControl1.Arule-base(asetofIf-Thenrules),whichcontainsafuzzylogicquantificationoftheexpert'slinguisticdescriptionofhowtoachievegoodcontrol.2.Aninferencemechanism(alsocalledan"inferenceengine"or"fuzzyinference"module),whichemulatestheexpert'sdecisionmakingininterpretingandapplyingknowledgeabouthowbesttocontroltheplant.3.Afuzzificationinterface,whichconvertscontrollerinputsintoinformationthattheinferencemechanismcaneasilyusetoactivateandapplyrules.4.Adefuzzificationinterface,whichconvertstheconclusionsoftheinferencemechanismintoactualinputsfortheprocess.Weintroduceeachofthecomponentsofthefuzzycontrollerforasimpleproblemofbalancinganinvertedpendulumonacart,asshowninFigure1.3.Here,ydenotestheanglethatthependulummakeswiththevertical(inradians),listhehalf-pendulumlength(inmeters),andu

istheforceinputthatmovesthecart(inNewtons).Wewilluser

todenotethedesiredangularpositionofthependulum.Thegoalistobalancethependulumintheuprightposition(i.e.,r=0)whenitinitiallystartswithsomenonzeroangleoffthevertical(i.e.,y≠0).Figure2.3InvertedpendulumonacartThisisaverysimpleandacademicnonlinearcontrolproblem,andmanygoodtechniquesalreadyexistforitssolution.Indeed,forthisstandardconfiguration,asimplePIDcontrollerworkswelleveninimplementation.Intheremainderofthissection,wewillusetheinvertedpendulumasaconvenientproblemtoillustratethedesignandbasicmechanicsoftheoperationofafuzzycontrolsystem.WewillalsousethisprobleminSection2.4todiscussmuchmoregeneralissuesinfuzzycontrolsystemdesignthatthereaderwillfindusefulformorechallengingapplications(e.g.,theonesinthenextchapter).2.2.1

ChoosingFuzzyControllerInputsandOutputs

Howdowechoosefuzzycontrollerinputsandoutputs?Considerahuman-in-the-loopwhoseresponsibilityistocontrolthependulum,asshowninFigure2.3.Thefuzzycontrolleristobedesignedtoautomatehowahumanexpertwhoissuccessfulatthistaskwouldcontrolthesystem.First,theexperttellsus(thedesignersofthefuzzycontroller)whatinformationsheorhewilluseasinputstothedecision-makingprocess.

Supposethatfortheinvertedpendulum,theexpert(thiscouldbeyou!)saysthatsheorhewilluse

Andasthevariablesonwhichtobasedecisions.Certainly,therearemanyotherchoices(e.g.,theintegraloftheerrorecouldalsobeused)butthischoicemakesgoodintuitivesense.Next,wemustidentifythecontrolledvariable.Fortheinvertedpendulum,weareallowedtocontrolonlytheforcethatmovesthecart,sothechoicehereissimple.

Formorecomplexapplications,thechoiceoftheinputstothecontrollerandoutputsofthecontroller(inputstotheplant)canbemoredifficult.Essentially,youwanttomakesurethatthecontrollerwillhavetheproperinformationavailabletobeabletomakegooddecisionsandhavepropercontrolinputstobeabletosteerthesysteminthedirectionsneededtobeabletoachievehigh-performanceoperation.Practicallyspeaking,accesstoinformationandtheabilitytoeffectivelycontrolthesystemoftencostmoney.Ifthedesignerbelievesthatproperinformationisnotavailableformakingcontroldecisions,heorshemayhavetoinvestinanothersensorthatcanprovideameasurementofanothersystemvariable.Alternatively,thedesignermayimplementsomefilteringorother,processingoftheplantoutputs.

Figure2.3HumancontrollinganinvertedpendulumonacartInaddition,ifthedesignerdeterminesthatthecurrentactuatorswillnotallowfortheprecisecontroloftheprocess,heorshemayneedtoinvestindesigningandimplementinganactuatorthatcanproperlyaffecttheprocess.Hence,whileinsomeacademicproblemsyoumaybegiventheplantinputsandoutputs,inmanypracticalsituationsyoumayhavesomeflexibilityintheirchoice.Thesechoicesaffectwhatinformationisavailableformakingon-linedecisionsaboutthecontrolofaprocessandhenceaffecthowwedesignafuzzycontroller.Oncethefuzzycontrollerinputsandoutputsarechosen,youmustdeterminewhatthereferenceinputsare.Fortheinvertedpendulum,thechoiceofthereferenceinputr=0isclear.Insomesituations,however,youmaywanttochooserassomenonzeroconstanttobalancethependulumintheoff-verticalposition.Todothis,thecontrollermustmaintainthecartataconstantvelocitysothatthependulumwillnotfall.

Figure2.4FuzzycontrollerforaninvertedpendulumonacartAfteralltheinputsandoutputsaredefinedforthefuzzycontroller,wecanspecifythefuzzycontrolsystem.Thefuzzycontrolsystemfortheinvertedpendulum,withourchoiceofinputsandoutputs,isshowninFigure2.4.Now,withinthisframeworkweseektoobtainadescriptionofhowtocontroltheprocess.Weseethenthatthechoiceoftheinputsandoutputsofthecontrollerplacescertainconstraintsontheremainderofthefuzzycontroldesignprocess.

Iftheproperinformationisnotprovidedtothefuzzycontroller,therewillbelittlehopeforbeingabletodesignagoodrule-baseorinferencemechanism.Moreover,eveniftheproperinformationisavailabletomakecontroldecisions,thiswillbeoflittleuseifthecontrollerisnotabletoproperlyaffecttheprocessvariablesviatheprocessinputs.

Itmustbeunderstoodthatthechoiceofthecontrollerinputsandoutputsisafundamentallyimportantpartofthecontroldesignprocess.

2.2.2PuttingControlKnowledgeintoRule-Bases

Howdoweputcontrolknowledgeintorule-bases?SupposethatthehumanexpertshowninFigure2.3providesadescriptionofhowbesttocontroltheplantinsomenaturallanguage(e.g.,EnglishorChineses).Weseektotakethis"linguistic"descriptionandloaditintothefuzzycontroller,asindicatedbythearrowinFigure2.4.

LinguisticdescriptionRulesRule-basesLinguisticDescriptions

linguisticvariablesThelinguisticdescriptionprovidedbytheexpertcangenerallybebrokenintoseveralparts.Therewillbe"linguisticvariables"thatdescribeeachofthetime-varyingfuzzycontrollerinputsandoutputs.Fortheinvertedpendulum,"error"describese(t)"change-in-error"describes"force"describesu(t)Notethatweusequotestoemphasizethatcertainwordsorphrasesarelinguisticdescriptions,andthatwehaveaddedthetimeindexto,forexample,e(t),toemphasizethatgenerallyevarieswithtime.Therearemanypossiblechoicesforthelinguisticdescriptionsforvariables.Somedesignersliketochoosethemsothattheyarequitedescriptivefordocumentationpurposes.

However,thiscansometimesleadtolongdescriptions.Othersseektokeepthelinguisticdescriptionsasshortaspossible(e.g.,using"e(t)"asthelinguisticvariablefore(t)),yetaccurateenoughsothattheyadequatelyrepresentthe