起重機(jī)外文翻譯

1、模糊控制式橋式起重機(jī)文摘:介紹了基于模糊控制的設(shè)計的吊車。而不是分析復(fù)雜非線性吊車系統(tǒng),該方法使用簡單但有效的方法來控制起重機(jī)。有雙模糊控制器這處理反饋信息,位置的架空起重機(jī)和擺動角的負(fù)荷,抑制搖擺和加快速度當(dāng)起重機(jī)運(yùn)輸沉重的負(fù)荷。這種方法簡化了設(shè)計過程的起重機(jī)控制器;此外,雙控制器方法減少了規(guī)則數(shù)當(dāng)完成模糊系統(tǒng)。最后,實(shí)驗結(jié)果通過起重機(jī)模型證明了該方案的有效性。關(guān)鍵詞:橋式起重機(jī);模糊控制1 介紹橋式吊車系統(tǒng)廣泛應(yīng)用于工業(yè)移動沉重的貨物。因此反搖擺和位置控制已成為需求作為核心技術(shù)自動化起重機(jī)系統(tǒng),能夠靈活的空間自動運(yùn)輸。起重機(jī)控制的目的是減少的擺動負(fù)載移動時電車到所需的位置盡可能快的。然而,

2、高架起重機(jī)已經(jīng)嚴(yán)重問題:起重機(jī)加速,需要運(yùn)動,總是引起不良負(fù)荷擺動。這樣的搖擺的負(fù)載通常會降低工作效率,有時導(dǎo)致負(fù)載損害賠償,甚至安全事故。因此,需要更快貨物裝卸要求精確控制起重機(jī)的運(yùn)動所以,它的動態(tài)性能得到了改進(jìn)。傳統(tǒng)上,起重機(jī)操作員驅(qū)動小車與這些步驟的加速運(yùn)動,勻速運(yùn)動,減速運(yùn)動,運(yùn)動和斷裂圖1顯示了距離速度參考曲線的傳統(tǒng)橋式吊車的操作4,5。有經(jīng)驗的起重機(jī)工人把電車精心保持負(fù)載從嚴(yán)重的搖擺。然而,保守的控制方法在現(xiàn)代工業(yè)無效。本研究提出了模糊雙控制器來控制小車起重機(jī)。很多的嘗試解決這個問題的搖擺的負(fù)載。他們中的大多數(shù)集中控制抑制負(fù)載擺動不考慮這個位置誤差在起重機(jī)運(yùn)動6。此外,幾個作者考慮

3、優(yōu)化技術(shù)來控制起重機(jī)。他們利用最小時間控制技術(shù)減少負(fù)載擺動(7 9)。因為負(fù)載的擺動取決于運(yùn)動和加速度的小車,減少周期時間和減少負(fù)荷擺動部分相互沖突的需求。除此之外,還有許多論文調(diào)查穩(wěn)定性問題的控制器設(shè)計10 12,但是這些研究缺乏實(shí)驗說明了有效性。本研究提出了一種實(shí)用的解決方案為反搖擺和精確位置控制的起重機(jī)。這個職位的電車、旋角的負(fù)荷及其分化是應(yīng)用于衍生出正確的控制輸入的電車起重機(jī)。兩個模糊邏輯控制器(FLC)是用來交易單獨(dú)與反饋信號,偏轉(zhuǎn)角和電車位置和他們的負(fù)效應(yīng)。模糊規(guī)則是根據(jù)經(jīng)驗設(shè)計的起重機(jī)工人了主要利用這種分離方法是大大減少的計算復(fù)雜性起重機(jī)控制系統(tǒng)。模糊規(guī)則數(shù)的總完成控制系統(tǒng)因此不

4、到規(guī)則數(shù)量的常規(guī)模糊系統(tǒng)。此外,當(dāng)設(shè)計提出的模糊控制器,沒有數(shù)學(xué)模型系統(tǒng)是起重機(jī)提前要求。因此,該算法是非常容易被實(shí)現(xiàn)。本文組織如下。第二單元回顧了提出了模糊雙控制器結(jié)構(gòu)對于起重機(jī)控制系統(tǒng)。在第三單元,實(shí)驗結(jié)果的起重機(jī)控制系統(tǒng)介紹了與傳統(tǒng)的比較起重機(jī)控制方法來說明提出的優(yōu)勢模糊的方法。本文結(jié)尾總結(jié)第四單元。2 模糊邏輯控制器對起重機(jī)物理設(shè)備的高架起重機(jī)系統(tǒng)圖在圖2。橋式吊車的長度模型5米,高是2米。塊圖,這反映在圖3,說明了提出了模糊邏輯起重機(jī)控制系統(tǒng)。在這個圖中,兩個編碼器的分辨率2000 PPR(脈沖/圓)安裝在起重機(jī)的小車檢測運(yùn)動位置和擺動角。反饋信號高架起重機(jī)作為輸入變量的模糊控制器。

5、有兩個類似的模糊邏輯控制器、位置控制器和搖擺控制器,分別與協(xié)議運(yùn)動位置和旋角信息來驅(qū)動電車起重機(jī)。雙模糊控制器作為像傳統(tǒng)的pd型控制器。在設(shè)計中錯誤ep及其衍生物誤差。ep選擇輸入語言變量的模糊位置控制器最初的小車位置設(shè)置為零,并在此基礎(chǔ)上指數(shù)k意味著好聽的樣品時間。此外,輸入語言變量的模糊控制器選用搖擺角e及其衍生角e>。左邊的擺動載荷被定義為積極的擺動,而正確的搖擺的負(fù)荷負(fù)搖擺。后程序模糊模糊化、推理過程和去模糊化,一個表示輸出語言變量的各自的位置控制器和swing控制器作為Up和u了。實(shí)際的動力來驅(qū)動小車被定義為u。設(shè)計程序都基于模糊位置控制器和搖擺控制器,描述了以下步驟13。步驟

6、1這一步證明輸入信號模糊變量。輸入和輸出空間劃分為若干個五個模糊區(qū)域互相重疊。一般來說,每個模糊區(qū)域標(biāo)記的語言術(shù)語。這些語言條件輸入變量的雙控制器了問,NS,AZ,PS和pl一使用三角形和梯形隸屬函數(shù)來證明輸入語言變量。圖4(a)(d)顯示各自的隸屬函數(shù)的 ep,ep,e和>e,分別獲得從小車位置編碼器和旋角編碼器。這個范圍的輸入變量ep和ep是- d / 6,d / 6和-200200,e和。e是 (-40年,40和-40、40,分別和范圍輸出變量和u是5,5。語言輸出變量方面,u被定義為五個模糊單例對象,這是代表視圖(e)、控制伺服驅(qū)動的直流電機(jī)驅(qū)動小車起重機(jī)。步驟2這一步介紹了模糊

7、化函數(shù)對于每個輸入變量來表達(dá)相關(guān)測量不確定度。一般來說,目的的模糊化函數(shù)f是解釋測量輸入變量,每個都是由一個實(shí)數(shù)表示,隨著越來越多的現(xiàn)實(shí)的模糊近似在各自的數(shù)量提出了運(yùn)用模糊單一功能模糊化過程。它意味著測量輸入變量用于模糊推理引擎直接。步驟3為了實(shí)現(xiàn)模糊邏輯控制系統(tǒng),既是位置控制器和搖擺控制器由二十5如果然后規(guī)則與下列形式A,B和C為模糊數(shù)的選擇設(shè)置模糊數(shù)的表示語句中, NS,AZ,PS和PL,符號“*”(6)意味著p或。如果部分的模糊規(guī)則形成的誤差和它的衍生物,其后果是決定根據(jù)起重機(jī)工人的經(jīng)驗和判斷。因為每個輸入變量有五個語言變量,總數(shù)量的可能都非沖突性的模糊規(guī)則位置控制器和搖擺控制器is2&

8、#215;52 = 50.了規(guī)則庫是顯示在表1和表。這些模糊規(guī)則可以很容易理解。假如設(shè)計師必須選擇合適的推理和去模糊化方法來設(shè)計模糊控制器。推理和去模糊化程序轉(zhuǎn)換結(jié)論模糊規(guī)則中獲得一個真正的號碼。由此產(chǎn)生的實(shí)數(shù),在某種意義上,總結(jié)了彈性約束對可能的值輸出變量的模糊集。對于每個輸入單例對(e *和e *),一個計算他們的兼容性j程度(e *,e *)與前期的標(biāo)定j.當(dāng)每個推理j(e *,e *)> 0,j觸發(fā)規(guī)則。至少一個規(guī)則解雇的原因是所有可能的輸入對的模糊控制器設(shè)計。敏敏馬克斯的推理方法用于總結(jié)所有的解雇規(guī)則在本文。為了獲得期望當(dāng)中的真正的價值,一個利用最常用的重心法將無法驗證推理的結(jié)

9、果。輸出的模糊位置和搖擺控制器和u上,分別。提出的雙控制器結(jié)構(gòu)提供了一種簡便的但有效的方法來控制模糊系統(tǒng)好。雙控制器在本文單獨(dú)的輸入條件模糊規(guī)則分為兩部分,位置和擺動角部分。因此,兩個位置控制器和搖擺控制器只有米/ 2模糊先行詞,每個包含N語言條款,那么必要的規(guī)則數(shù)量來滿足該系統(tǒng)2 * NM / 2。規(guī)則數(shù)大大減少。對于例,既是位置控制器和搖擺控制器有兩個輸入語言變量。四個輸入語言變量被劃分成五個部分每個;因此必要的規(guī)則來控制起重機(jī)數(shù)量減少到50。與傳統(tǒng)的模糊方案相比,分離雙控制器方法有助于使模糊比平時更容易控制。此外,提出了雙控制器結(jié)構(gòu)適用于任何使用模糊控制應(yīng)用程序。3實(shí)驗結(jié)果有幾個實(shí)驗結(jié)果

10、說明基于模糊控制的優(yōu)點(diǎn)起重機(jī)系統(tǒng)編碼器的數(shù)據(jù)讓我們知道真正的位置和搖擺的電車角的負(fù)載在任何時間。手續(xù)后模糊化、模糊推理和去模糊化,每個模糊控制器得到控制的價值。作者使用求和的值的控制,u和來驅(qū)動電車。模糊控制器將控制電車到現(xiàn)有的距離的目標(biāo)是更少的than0.01 * d,與此同時,旋角的負(fù)載是把單位少。這個符號d是初始距離的目標(biāo)。使用2000 ppr編碼器、一個單位等于360/2000的搖擺度。實(shí)驗說明了模糊方案的優(yōu)點(diǎn)。我們使用傳統(tǒng)的方法來控制起重機(jī)比較的目的。當(dāng)操作起重機(jī)根據(jù)速度參考曲線如所示圖1、摩擦和限制機(jī)制將使精確的電車停在固定位置成為不可能的,因此額外的制動電車到目標(biāo)是必要的。我們安

11、裝了了一個長為120cm的軟線在這個實(shí)驗。負(fù)載運(yùn)輸是3公斤了距離目標(biāo)是39500歸一化單元,即。d =39500年。假設(shè)位置歸一化單位車是0在一開始,圖5(一個)顯示位置的小車和負(fù)載的擺動角度、運(yùn)送沉重的負(fù)荷傳統(tǒng)的控制方法。一個可以很容易發(fā)現(xiàn)搖擺太嚴(yán)重破壞載荷圖5(b)顯示了結(jié)果模糊控制基礎(chǔ)的方法。很明顯,電車停在正確的位置和swing是微不足道的,與此同時,運(yùn)輸時間的負(fù)載是縮短了穩(wěn)態(tài)誤差是由于一些空氣阻力造成的。當(dāng)運(yùn)輸負(fù)載向目的地,這是更多的難以抑制的影響特別是傳統(tǒng)方法然而,圖5(b)表明,負(fù)載的影響模糊方法也很光滑。表演的解決起重機(jī)在定位和抑制的影響的負(fù)載比傳統(tǒng)方案。4結(jié)論本文提供了基于雙

12、控制器的模糊控制橋式吊車。應(yīng)用該法,不僅運(yùn)輸速度增加但同樣的擺動載荷是非常光滑。此外,提出的方法將輸入語言變量進(jìn)入兩部分,位置變量和擺動變量因此,只有五十規(guī)則是必要的,以完成系統(tǒng)。這個提出了分離算法有助于減少計算復(fù)雜性的模糊控制器。實(shí)驗結(jié)果表明,所提出的方法提高了模糊控制系統(tǒng)的性能橋式吊車。工作效率得以提高。1介紹移動液壓機(jī)器必須面對的問題的低頻振蕩在行動,特別是在復(fù)雜的情況下。振蕩阻尼液壓系統(tǒng)實(shí)現(xiàn)了通過各種活動或高質(zhì)量的控制技術(shù)1。由于操作簡單,容易設(shè)計,便宜的維護(hù)、高成本效率,傳統(tǒng)的PID控制,已被廣泛應(yīng)用于超過90情況下線性或準(zhǔn)線性的工業(yè)過程系統(tǒng)2。然而,傳統(tǒng)的PID(或PI,PD)控制

13、器不能令人滿意地工作為高階時變線性或非線性系統(tǒng)。許多優(yōu)化PID參數(shù)有法律提出了滿足一個特定的需求在過去幾十年。盡管齊格勒尼科爾斯調(diào)優(yōu)(ZNT)方法有一個好的能力的負(fù)載擾動衰減,它有一個嗎可憐的階段保證金。因此,PID與ZNT可能產(chǎn)生一個大過火和長期穩(wěn)定時間在階躍響應(yīng)。因此,這種PID不能提供令人滿意的控制性能。各種各樣的模糊控制應(yīng)用程序已經(jīng)被發(fā)現(xiàn)全球3?；谡Z言代替?zhèn)鹘y(tǒng)數(shù)學(xué)模型、模糊邏輯的基礎(chǔ)上設(shè)計專家的經(jīng)驗,決策和控制行為。模糊邏輯可以被添加到自動控制系統(tǒng)處理不僅線性也模糊系統(tǒng)控制的非線性。近年來,研究集成模糊邏輯與PID控制導(dǎo)致復(fù)合模糊PID(或模糊pi模糊pd控制4。組合形式的PID和模

14、糊邏輯是非常靈活的。為了提高提示阻尼的HCBS在工作期間,一個PI控制器基于FSPW下面發(fā)達(dá)。限制輸出信號和防止風(fēng)起,一個有限的半積分器(LSI)引入到集成的部分提出控制器5。通過使用一個攝像頭和光電電子反饋控制范圍,提示阻尼的HCBS可以人為調(diào)整。仿真結(jié)果驗證了所提出的控制有效性策略。2模型的液壓起重機(jī)臂系統(tǒng)由于機(jī)械的弱點(diǎn)HCBS,沒有真實(shí)準(zhǔn)確的提示位置可以達(dá)成,和一個相當(dāng)?shù)偷淖枘嵴駝酉到y(tǒng)中發(fā)生的。共振頻率的液壓和機(jī)械系統(tǒng)呆在一個狹窄的范圍。這些導(dǎo)致振蕩收到2005年11月17日,修訂后的2006年6月19日。這項工作由湖南省自然科學(xué)基金(No.04JJ6033)和科研基金的湖南省教育部門(

15、沒有。03 c066)。328 y楊et al。/雜志的控制理論和應(yīng)用4(2006)327 - 330問題。通過使用一個光電范圍相機(jī),它是可能的來定位垂直位置的起重機(jī)提示和com -pensate弱者液壓機(jī)械阻尼。這個版本提卡位移之間攝影機(jī)放在起重機(jī)提示和對象的位置是由光電測量范圍相機(jī)。運(yùn)動學(xué)的HCBS結(jié)構(gòu)得到使用信號傳感器在液壓位置氣缸和幾何描述的HCBS。然后,逆運(yùn)動學(xué)計算的HCBS確定伺服閥控制行動。一個HCBS顯示在圖1。3設(shè)計PI控制器基于FSPW以下基于FSPW PI控制器設(shè)計了后HCBS圖3所示。提出的控制系統(tǒng)由一系列攝影機(jī)安裝在夾的起重機(jī),2個閥控PI控制器,包括一個比例前饋控

16、制器基于FSPW以下和大規(guī)模集成電路控制器。一個復(fù)雜的非線性系統(tǒng)通常是由不同類型的零件在不同的建模,開發(fā)擔(dān)憂的ronments7。作為一個HCBS,其液壓部件和結(jié)構(gòu)建模,通過AMESIM DSH +,WINSIMU HOPSAN iti sim,或,而不同的控制算法在MATLAB或VISSIM實(shí)現(xiàn)。目前,許多專用的建模軟件是僅由特定的環(huán)境。為了提高設(shè)計效率和質(zhì)量,這些模型需要耦合和模擬。這將導(dǎo)致不同的聯(lián)合仿真模型連接器可以獨(dú)立工作或合作。在這里, MATLAB / SIMULINK和HOPSAN是用來做協(xié)同控制仿真。液壓系統(tǒng)模型產(chǎn)生HOPSAN和控制仿真控制器設(shè)計時被做在MATLAB / SI

17、MULINK仿真。某些擴(kuò)展,活塞桿的位置反映了控制精度,而動力性能HCBS映照出加速度的起重機(jī)建議包含信息在振蕩級或阻尼以及響應(yīng)速度。仿真結(jié)果無花果所示。4和5。在這兩個數(shù)據(jù),曲線1、2和3代表參考輸入,活塞桿的位置和加速度的起重機(jī)提示,分別。參考輸入到系統(tǒng)Xp2r = 0.26米。圖4是結(jié)果在一個純粹的位置比例反饋控制,而圖5是,根據(jù)提出的PI控制。比較圖5中的結(jié)果,這些視圖,它可以被發(fā)現(xiàn),提出的PI控制具有優(yōu)越的能力使HCBS響應(yīng)速度,同時保持一個好的阻尼。通過整合一個FSPW進(jìn)PI控制器,液壓起重機(jī)臂系統(tǒng)可以改善其以下設(shè)置點(diǎn)特征以及提示阻尼。這提高了HCBS在生產(chǎn)率和操作安全。Abstr

18、act :This paper presents fuzzy based design for the control of overhead crane. Instead of analyzing the complexnonlinear crane system the proposed approach uses simple but effective way to control the crane There are twin fuzzy controllerswhich deal with the feedback information the position of trol

19、ley crane and the swing angle of load to suppress the sway andaccelerate the speed when the crane transports the heavy load This approach simplifies the designing procedure of crane controller;besides the twin controller method reduces the rule number when fulfilling the fuzzy system. Finally experi

20、mental results throughthe crane model demonstrate the effectiveness of the scheme.Keywords: Overhead Crane; Fuzzy Control1IntroductionThe overhead crane system is widely used in industry for moving heavy cargos. Thus anti_ sway and position control have become the requirements as a core technology f

21、or automated crane system that are capable of flexible spatial automatic conveyance. The purpose of crane control is to reduce the swing of the load while moving the trolley to the desired position as fast as possible .However, the overhead crane has serious problems: the crane acceleration, require

22、d for motion, always induces undesirable load swing. Such swing of load usually degrades work efficiency and sometimes causes load damages and even safety accidents. Thus, the need for fastercargo handling requires the precise control of crane motion so that its dynamic performance is improved 13. T

23、raditionally, the crane operator drives the trolley with the steps of accelerated motion, uniform motion, decelerated motion creped motion and breaking.Fig.1 shows the distance_ speed reference curve of conventional operation of overhead crane 4,5.The experienced crane workers drive the trolley care

24、fully to keep the load from severe swing. However ,the conservative control method is ineffective in modern industry. This study proposed the fuzzy twin controllers to control the trolley crane.Various attempts have been made to solve the problem of swing of load. Most of them focus the control on s

25、uppression of load swing without considering the position error in crane motion 6.Besides, several authors have considered optimization techniques to control the cranes. They have used minimal time control technique to minimize the load swing 79.Since the swing of load depends on the motion and acce

26、leration of the trolley, minimizing the cycle time and minimizing the load swing are partially conflicting requirements. Besides, there are many papers investigating the stability problem of controller design 1012,but those researches lack experiments to illustrate the effectiveness.This study prese

27、nts a practical solution for the anti_ swing and precise position control for the cranes. The position of trolley, swing angle of load and their differentiations are applied to derive the proper control input of the trolley crane. Two fuzzy logic controllers (FLC) are used to deal separately with th

28、e feedback signals, swing angle and trolley position and their differentiations. The fuzzy rules are designed according to the experience of crane workers. The main advantage of this separated approach is to greatly reduceTheory andApplications3(2005)266-270the computational complexity of the crane

29、control system. The total fuzzy rule number for fulfilling the control system is therefore less than the rule number of conventional fuzzy system. Besides, when designing the proposed fuzzy controllers no mathematical model of the crane system is required in advance Thus the proposed algorithm is ve

30、ry easy to be implemented. This paper is organized as follows.Section2reviews the proposed fuzzy twin controller structure for crane control system In section3,experimental results of crane control system are presented in comparison with the conventional crane control method to illustrate the advant

31、age of proposed fuzzy approach This paper concludes with a summary in section4.2Fuzzy logic controllers for craneThe physical apparatus of the overhead crane system is pictured in Fig.2.The length of overhead crane model is five meters, and the height is two meters. The block diagram, which is repre

32、sented in Fig.3, illustrates the proposed fuzzy logic crane control system In this diagram, two encoders with the resolution2000PPR (pulses per round) are installed on the trolley of crane to detect the motion position and swing angle The feedback signals from overhead crane act as the input variabl

33、es of fuzzy controllers.here are two similar fuzzy logic controllers position controller and swing controller which deal separately with the motion position and swing angle information to drive the trolley crane The twin fuzzy controllers work as like the conventional PD type controllers. In the des

34、ign, the error epand its derivative error epare selected as the input linguistic variables of fuzzy position controllerThe initial trolley position is set to zero in this paper. The indexkmeans the kth sample time. Besides, the input linguistic variables of fuzzy swing controller are selected as the

35、 swing angle e and its derivative. E he left swing of load is defined as positive swing, while the right swing of load is negative swing. After the procedures of fuzzy fuzzification, inference process and defuzzification, one denotes the output linguistic variables of the respective position control

36、ler and swing controllers as upand u.The actual power to drive the trolley is defined as u.The designing procedures for both the fuzzy_ based position controller and swing controller, are described in the following steps 13.Step1This step fuzzifies the input signals into fuzzy variables. The input a

37、nd output space are partitioned into five fuzzy regions overlapping each other .In general, each fuzzy region is labeled by a linguistic term. These linguistic terms for the input variables of the twin controllers are given as NL, NS, AZ, PS, and PL. One uses the triangular and trapezoidal membershi

38、p functions to fuzzify the input linguistic variables. Figs.4(a)(d) show the respective membership functions of ep,. ep, e and. e, which were obtained respectively from the trolley position encoder and swing angle encoder. The ranges of input variables ep and. Ep are-d/6,d/6and -200,200,e and. E are

39、 -40,40 and -40,40,respectively, and the ranges of the output variable up and u are -5,5.The linguistic terms of output variablesup and u are defined as five fuzzy singletons, which are represented in Fig.4(e),controlling the servo driver of DC_ motor to drive the trolley crane.Step2This step introd

40、uces the fuzzification function for each input variable to express the associated measurement uncertainty. Generally speaking, the purpose of the fuzzification function f is to interpret measurement of input variables, each is expressed by a real number ,as more realistic fuzzy approximations of the

41、 respective number. The proposed paper applies fuzzy singleton function in the fuzzification process. It means that the measurements for input variables are employed in fuzzy inference engine directly.Step3In order to fulfill the fuzzy logic control system, Both the position controller and the swing

42、 controller consist of twenty_ five IF_THEN rules with the following formWhere A, B and C are fuzzy numbers chosen from the set of fuzzy numbers that represent the linguistic states NL, NS,AZ,PS and PL, the notation“*”in (6) means p or.The IF part of the fuzzy rules are formed by the error and its d

43、erivative, and the consequences are decided according to the crane workers experience and judgment. Since each input variable has five linguistic variables, the total number of possible non conflicting fuzzy rules for both position controller and swing controller is2×52=50.The rule bases are sh

44、own in Table1and Table2.These fuzzy rules can be understood very easily.Step4The designer has to select suitable inference and defuzzification methods for designing fuzzy controllers. The inference and defuzzification procedures convert the conclusions obtained from fuzzy rules to a single real numb

45、er. The resulting real number, in some sense, summarizes the elastic constraint imposed on possible values of the output variable by the fuzzy set. For each input singleton pair(e*and e*),one calculates the degree of their compatibility j(e*,e*)with the antecedent of each inference rule j. Whenj(e*,

46、e*) >0,thejth rule is fired. At least one rule is fired for all possible input pair in the fuzzy controller design. The min_ min_ max inference method is used to conclude all the fired rules in this paper. In order to obtain the defuzzified real value, one utilizes the most frequently used centro

47、id method to defuzzify the inference results. The outputs of the fuzzy position and swing controllers are upand u,respectively.The proposed twin controller structure provides an easy but effective way to control the fuzzy system well. The twin controllers in this paper separate the input antecedents

48、 of fuzzy rules into two parts, position and swing angle parts. Hence, both position controller and swing controller have onlyM/2fuzzy antecedents, each containing Nlinguistic terms, then the necessary rule number to fulfill the system is 2*NM/2.The rule number is greatly reduced. For example, both

49、the position controller and swing controller have two input linguistic variables. The four input linguistic variables are partitioned into five parts each; hence the necessary rule number to control the crane is reduced to 50.When compared with traditional fuzzy schemes, the separated twin controlle

50、rs method helps to make fuzzy control easier than usual. Besides, the proposed twin controllers structure is suitable for any use of fuzzy control applications. 3Experimental results There are several experimental results illustrating the advantages of fuzzy based crane control system. Encoders data

51、 make us know the real position of trolley and swing angle of load at any time. After the procedures of fuzzification, fuzzy inference and defuzzification, each fuzzy controller gets a control value. The authors use the summation of the control values, upand u,to drive the trolley. The fuzzy control

52、lers will control the trolley until the existing distance to goal is less than0.01*d, meanwhile the swing angle of load is less than10units. The notation d is the initial distance to the goal .For using 2000PPR encoders, a unit of swing equals to360/2000 degrees.Experiment illustrates the advantages

53、 of fuzzy scheme. We uses the conventional method to control the crane for the purpose of comparison. When operating the crane according to the speed reference curve such as shown in Fig.1,the friction and limitation of mechanism will make the trolley precisely stop at the fixed position become impo

54、ssible, hence additional braking the trolley to the goal is necessary .We employed a flexible wire with120cm long in the experiment .The load for transportation is3kg.The distance to goal is 39500 normalized units, i. e .d = 39500.Suppose that position of trolley is 0 normalized unit at the start,Fi

55、g.5(a) shows the position of trolley and swing angle of load when transporting the heavy load by conventional control method. One can easily find that the swing is too severe to damage the load.Fig.5(b) shows the result of fuzzy based approach .It is obvious that the trolley stops at the correct pos

56、ition and the swing is negligible, and meanwhile the transporting time of load is shortened. The steady state error is caused by some airstreams. When transporting the load towards the destination ,it is more difficult to restrain the sway especially by conventional approach.However,Fig.5(b) shows t

57、hat the load sway by fuzzy approach is also very smooth .The performances for fixing the crane on fixed position and restraining the sway of load are better than conventional scheme. Fig.5Transporting the3kg load with120cm flexible wire. 4ConclusionThis paper provides fuzzy based twin controllers to

58、 269C.CHANG et al./Journal of Control Theory andApplications3(2005)266-270control the overhead crane. By applying the proposed method ,not only the transporting speed is increased but also the swing of load is very smooth. Moreover, the proposed method separates the input linguistic variables into two parts, position variables and swing variables. Hence, only fifty rules are necessary to fulfill the system. The proposed separated algorithm helps to reduce the computational complexity of the fuzzy controller. Experimental results prove that the proposed