橋式起重機(jī)模糊控制外文翻譯語序已改好

1、 中 北 大 學(xué)畢業(yè)設(shè)計外文資料翻譯原文名稱A practical fuzzy controllers scheme of overhead crane中文名稱基于模糊控制式的橋式起重機(jī)原文來源：Chengyuan CHANG, Shihwei HSU, Kuohung CHIANG (Department of Electronic Engineering,ChingYun University,Jhongli,Taoyuan320,Taiwan,China)學(xué)生姓名：甘笛學(xué)號：1302014244學(xué) 院：機(jī)械與動力工程專 業(yè)：機(jī)械工程指導(dǎo)教師:王宗彥 2017 年 6 月 1 日基于模糊控

2、制式的橋式起重機(jī)摘要本文提出了一種基于模糊設(shè)計的橋式起重機(jī)控制設(shè)計。該方法是采用簡單且有效的方式來控制起重機(jī)，而不是分析復(fù)雜非線性起重機(jī)系統(tǒng)。本次設(shè)計是用雙模糊控制器的方法處理反饋信息。根據(jù)軌道起重機(jī)的位置和負(fù)載的擺動角度來壓制搖擺，加快起重機(jī)運(yùn)輸時的速度。這種方式簡化了起重機(jī)控制的設(shè)計程序；此外，當(dāng)完成模糊系統(tǒng)時，雙控制器的方法減少了指令數(shù)。最后，實(shí)驗(yàn)結(jié)果通過了起重機(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 介紹橋式起重機(jī)系統(tǒng)在工業(yè)上廣泛應(yīng)用，用作搬運(yùn)重物。因此，作為起重機(jī)自動化控制系統(tǒng)的核心技術(shù)，反搖擺和位置控制已成為一種必要條件,以保證能夠靈活的進(jìn)行空間上的自動化運(yùn)行。

3、起重機(jī)控制的目的是降低擺動負(fù)載，盡可能在移動時，使小車快速的處于所需要到達(dá)的位置。然而,高架起重機(jī)已經(jīng)出現(xiàn)了嚴(yán)重的問題：起重機(jī)的加速、所需要的運(yùn)行動作,總是引起不良作用的負(fù)荷擺動。這樣的搖擺負(fù)載通常會降低工作效率,有時會導(dǎo)致負(fù)載損耗,甚至造成安全事故。因此,為滿足更快速裝卸貨物的要求，需要精確控制起重機(jī)的運(yùn)行。所以,橋式起重機(jī)的動態(tài)性能得到了提升1-3。傳統(tǒng)上,起重機(jī)的操作員通過加速運(yùn)動、勻速運(yùn)動、減速運(yùn)動、周期運(yùn)動和制動來駕駛軌道吊車。如圖1所示，顯示了傳統(tǒng)橋式起重機(jī)的操作過程中，距離/速度關(guān)系的參考曲線4-5。有經(jīng)驗(yàn)的起重機(jī)工人駕駛小車時，能小心地使負(fù)載免受劇烈擺動影響。然而，保守的控制方

4、法在現(xiàn)代工業(yè)中是沒有作用的。本研究提出了用模糊雙控制器來控制有軌吊運(yùn)起重機(jī)。各種各樣的嘗試都是為了解決負(fù)載的擺動問題。其中的大多數(shù)都集中控制能力于負(fù)載擺動的抑制，而不考慮在起重機(jī)運(yùn)行中的位置誤差6。此外，還有一些作者考慮了通過優(yōu)化技術(shù)來控制起重機(jī)。他們利用的控制技術(shù)，能以最少的時間來減少負(fù)載擺動7 - 9。因?yàn)樨?fù)載的擺動取決于有軌吊車的運(yùn)行及其加速度,最大限度的減少周期時間和減少負(fù)荷擺動是部分上相互矛盾的要求。除此之外,還存在許多研究控制器設(shè)計中穩(wěn)定性問題的論文10-12,但是這些研究都缺少實(shí)驗(yàn)來說明其有效性。 本次研究提出了一種實(shí)用可行的解決方案，用于起重機(jī)的反搖擺和精確位置控制。有軌吊車的

5、位置、負(fù)載的擺動角度和它們之間的差異，被應(yīng)用于獲得有軌起重機(jī)的正確控制輸入。兩個模糊邏輯控制器(FLC)是用來分別處理反饋信號、擺動角度、有軌吊車的位置和他們之間的區(qū)別。這種模糊規(guī)則，是根據(jù)起重機(jī)工作人員的經(jīng)驗(yàn)才設(shè)計出來的。這種分離方法的主要優(yōu)點(diǎn)是，大大減少理論和應(yīng)用(2005)起重機(jī)控制系統(tǒng)的計算復(fù)雜度。因此，實(shí)現(xiàn)控制系統(tǒng)的模糊規(guī)則數(shù)小于常規(guī)模糊系統(tǒng)的規(guī)則數(shù)量。此外，在設(shè)計模糊控制器的時候，不需要對起重機(jī)系統(tǒng)的數(shù)學(xué)模型進(jìn)行數(shù)學(xué)建模。因此，提出來的這種算法是很容易實(shí)現(xiàn)的。本文組織如下。第二單元回顧了，被提出的基于起重機(jī)控制系統(tǒng)的模糊雙控制器結(jié)構(gòu)。在第三單元,通過對起重機(jī)控制系統(tǒng)的實(shí)驗(yàn)結(jié)果與傳統(tǒng)

6、的起重機(jī)控制方法進(jìn)行比較，說明了模糊方法的優(yōu)點(diǎn)。本文在結(jié)尾處，總結(jié)了第四單元的內(nèi)容。2 起重機(jī)上的模糊邏輯控制器橋式起重機(jī)系統(tǒng)的物理裝置圖，如圖2所示。橋式起重機(jī)模型的長度是5 m，高度是2 m。如圖3所示，其框圖說明了，在本圖中提出的模糊邏輯起重機(jī)控制系統(tǒng)，在這個圖中,兩個分辨率為2000 PPR(脈沖/圓)的編碼器分別安裝在起重機(jī)的軌道上，以探測運(yùn)動位置和擺動角度。以橋式起重機(jī)的反饋信號作為模糊控制器的輸入變量。有兩種相似的模糊邏輯控制器，位置控制器和擺動控制器，分別對運(yùn)動位置和擺動角度信息進(jìn)行處理。雙模糊控制器與傳統(tǒng)的PD型控制器相似。在設(shè)計中，誤差ep及其衍生誤差，被選擇為模糊位置控制

7、器的輸入語言變量。在本文中，初始的有軌吊車位置設(shè)置為0。索引k表示第k個樣本時間。此外,模糊控制器的輸入語言變量，被選用為擺動角e及其衍生角E。負(fù)載的左擺動定義為正擺動，而負(fù)載的右擺動定義為負(fù)擺動。在模糊的程序模糊化后，推理過程和去模糊化,指示相應(yīng)的位置控制器和擺動控制器的輸出語言變量作為Up和u。驅(qū)動有軌吊車的實(shí)際力量被定義為u。下面的步驟介紹了基于模糊式的位置控制器和擺動控制器的設(shè)計過程13。步驟1:這一步將輸入信號模糊化，得到模糊變量。輸入和輸出空間劃分為五個模糊區(qū)域，互相重疊。一般來說,每個模糊區(qū)域都有一個特定的語言術(shù)語。這兩個控制器的輸入變量的語言術(shù)語分別為NL、NS、AZ、PS和P

8、L。是使用三角形和梯形之類的從屬函數(shù)來使輸入語言變量模糊化。如圖4(a)(d)所示，表示的是ep、Ep、e和E各自的從屬函數(shù)，分別是從有軌吊車的位置編碼器和擺動角編碼器中獲得。輸入變量ep和Ep的范圍是- d / 6 d / 6和-200200,e和E的范圍是-40,40和-40,40,輸出變量Up和u的范圍是-5,5。語言的輸出變量up和u被定義為模糊化的單例5。在圖4(e)中，控制DC_馬達(dá)的伺服驅(qū)動器來驅(qū)動軌道橋式起重機(jī)。 步驟2：本步驟介紹了每個輸入變量的模糊化函數(shù)，以表達(dá)相關(guān)的測量不確定性。一般來說，模糊化函數(shù)f的目的是解釋輸入變量的度量，每一個都是由一個實(shí)數(shù)表示的，這是相對于相應(yīng)數(shù)

9、字的更現(xiàn)實(shí)的模糊近似。提出了模糊單例函數(shù)在模糊化過程中的應(yīng)用。這意味著對輸入變量的測量直接應(yīng)用于模糊推理引擎。步驟3為了實(shí)現(xiàn)模糊邏輯控制系統(tǒng),位置控制器和擺動控制器都是由二十五個IF-THEN指令集，如下式A,B和C為模糊數(shù)，在設(shè)置的模糊數(shù)的代表性學(xué)術(shù)語, NS、AZ、PS和PL中選擇,其符號“*”(6)意味著p或。模糊規(guī)則的一部分是由誤差及其衍生物構(gòu)成的，其后果取決于起重機(jī)工人的經(jīng)驗(yàn)和判斷。因?yàn)槊總€輸入變量有五個語言變量，其總數(shù)可能是非沖突的位置控制器和擺動控制器的模糊指令數(shù)之和，即有可能2×52 = 50?？刂浦噶罴?，如表1和表2中所示。這些模糊式的指令可以很容易地理解。步驟4：

10、假設(shè)計者必須選擇合適的推理和模糊化方法來設(shè)計模糊控制器。推理和去模糊化過程將從模糊規(guī)則獲得的結(jié)論轉(zhuǎn)換為一個實(shí)數(shù)。在某種意義上，產(chǎn)生的實(shí)數(shù)，總結(jié)了由模糊集對輸出變量的可能值施加的彈性約束。對于每個輸入單例對(e *和e *),計算他們的兼容性程度j(e *,e *)與判斷的前提指令j.每當(dāng)判斷j(e *,e *)> 0,則指令j觸發(fā)。在模糊控制器設(shè)計中，對所有可能的輸入對至少有一條指令。利用最小的最小值推理方法來完成本文的所有觸發(fā)指令。為了獲得去模糊化的真實(shí)值，利用最常用的重心法來使判斷結(jié)果去模糊化。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

11、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(yàn)結(jié)果有幾個實(shí)驗(yàn)結(jié)果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

12、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(yàn)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。我們安裝了了一個長為120cm的軟線在這個實(shí)驗(yàn)。負(fù)載運(yùn)輸是3公斤了距離目標(biāo)是39500歸一化單元,

13、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)論本文為控制理論與應(yīng)用(2005)266 - 270中269提供了基于模糊式雙控制器控制的橋式起重機(jī)。通過應(yīng)

14、用該方法，不僅提高了運(yùn)輸速度，而且載荷的擺動也非常平穩(wěn)。此外，該方法將輸入語言變量分為兩部分，位置變量和swing變量。因此，只有50條規(guī)則才能實(shí)現(xiàn)這個系統(tǒng)。提出的離散算法有助于減少模糊控制器的計算復(fù)雜度。實(shí)驗(yàn)結(jié)果表明，該方法提高了橋式起重機(jī)的模糊控制系統(tǒng)的性能。因此，該設(shè)計提高了工作效率。Abstract :This paper presents fuzzy based design for the control of overhead crane. Instead of analyzing the complexnonlinear crane system the proposed ap

15、proach uses simple but effective way to control the crane There are twin fuzzy controllerswhich deal with the feedback information .the position of trolley crane and the swing angle of load to suppress the sway and accelerate the speed when the crane transports the heavy load .This approach simplifi

16、es the designing procedure of crane controller;besides the twin controller method reduces the rule number when fulfilling the fuzzy system. Finally experimental results throughthe crane model demonstrate the effectiveness of the scheme.Keywords: Overhead Crane; Fuzzy Control1IntroductionThe overhead

17、 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 for automated crane system that are capable of flexible spatial automatic conveyance. The purpose of crane control is to reduce the swing of the lo

18、ad while moving the trolley to the desired position as fast as possible .However, the overhead crane has serious problems: the crane acceleration, required for motion, always induces undesirable load swing. Such swing of load usually degrades work efficiency and sometimes causes load damages and eve

19、n safety accidents. Thus, the need for fastercargo handling requires the precise control of crane motion so that its dynamic performance is improved 13. Traditionally, the crane operator drives the trolley with the steps of accelerated motion, uniform motion, decelerated motion creped motion and bre

20、aking.Fig.1 shows the distance_ speed reference curve of conventional operation of overhead crane 4,5.The experienced crane workers drive the trolley carefully to keep the load from severe swing. However ,the conservative control method is ineffective in modern industry. This study proposed the fuzz

21、y 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 suppression of load swing without considering the position error in crane motion 6.Besides, several authors have considered optimization techniques

22、 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 acceleration of the trolley, minimizing the cycle time and minimizing the load swing are partially conflicting requirements. Besides, there are many p

23、apers investigating the stability problem of controller design 1012,but those researches lack experiments to illustrate the effectiveness.This study presents a practical solution for the anti_ swing and precise position control for the cranes. The position of trolley, swing angle of load and their d

24、ifferentiations are applied to derive the proper control input of the trolley crane. Two fuzzy logic controllers (FLC) are used to deal separately with the feedback signals, swing angle and trolley position and their differentiations. The fuzzy rules are designed according to the experience of crane

25、 workers. The main advantage of this separated approach is to greatly reduceTheory andApplications3(2005)266-270the computational complexity of the crane control system. The total fuzzy rule number for fulfilling the control system is therefore less than the rule number of conventional fuzzy system.

26、 Besides, when designing the proposed fuzzy controllers no mathematical model of the crane system is required in advance Thus the proposed algorithm is very easy to be implemented. This paper is organized as follows.Section2reviews the proposed fuzzy twin controller structure for crane control syste

27、m In section3,experimental results of crane control system are presented in comparison with the conventional crane control method to illustrate the advantage of proposed fuzzy approach This paper concludes with a summary in section4.2Fuzzy logic controllers for craneThe physical apparatus of the ove

28、rhead 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 represented in Fig.3, illustrates the proposed fuzzy logic crane control system In this diagram, two encoders with the resolution2000PPR (pulses per ro

29、und) 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 variables of fuzzy controllers.There are two similar fuzzy logic controllers position controller and swing controller which deal separately with the mot

30、ion position and swing angle information to drive the trolley crane .The twin fuzzy controllers work as like the conventional PD type controllers. In the design, the error epand its derivative error epare selected as the input linguistic variables of fuzzy position controller.The initial trolley pos

31、ition is set to zero in this paper. The index k means the kth sample time. Besides, the input linguistic variables of fuzzy swing controller are selected as the swing angle e and its derivative E .The left swing of load is defined as positive swing, while the right swing of load is negative swing. A

32、fter the procedures of fuzzy fuzzification, inference process and defuzzification, one denotes the output linguistic variables of the respective position controller and swing controllers as upand u.The actual power to drive the trolley is defined as u.The designing procedures for both the fuzzy_ bas

33、ed position controller and swing controller, are described in the following steps 13.Step1This step fuzzifies the input signals into fuzzy variables. The input and output space are partitioned into five fuzzy regions overlapping each other .In general, each fuzzy region is labeled by a linguistic te

34、rm. 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 membership functions to fuzzify the input linguistic variables. Figs.4(a)(d) show the respective membership functions of ep,. ep, e and. e, which we

35、re 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 -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 a

36、re 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 introduces the fuzzification function for each input variable to express the associated measurement uncertainty. Generally speaking, the purpose

37、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 respective number. The proposed paper applies fuzzy singleton function in the fuzzification process. It means that the measurements for in

38、put variables are employed in fuzzy inference engine directly.Step3In order to fulfill the fuzzy logic control system, Both the position controller and the swing 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 nu

39、mbers 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 derivative, and the consequences are decided according to the crane workers experience and judgment. Since each input variable has five ling

40、uistic variables, the total number of possible non conflicting fuzzy rules for both position controller and swing controller is2×52=50.The rule bases are shown in Table1and Table2.These fuzzy rules can be understood very easily.Step4The designer has to select suitable inference and defuzzificat

41、ion methods for designing fuzzy controllers. The inference and defuzzification procedures convert the conclusions obtained from fuzzy rules to a single real number. The resulting real number, in some sense, summarizes the elastic constraint imposed on possible values of the output variable by the fu

42、zzy 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*,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 in

43、ference 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 centroid method to defuzzify the inference results. The outputs of the fuzzy position and swing controllers are upand u,respectively.The proposed

44、 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 of fuzzy rules into two parts, position and swing angle parts. Hence, both position controller and swing controller have onlyM/2fuzzy ante

45、cedents, 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 the position controller and swing controller have two input linguistic variables. The four input linguistic variables are partitioned into

46、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 controllers method helps to make fuzzy control easier than usual. Besides, the proposed twin controllers structure is suitable for any use of fuzzy

47、control applications. 3Experimental results There are several experimental results illustrating the advantages of fuzzy based crane control system. Encoders data make us know the real position of trolley and swing angle of load at any time. After the procedures of fuzzification, fuzzy inference and

48、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 controllers will control the trolley until the existing distance to goal is less than0.01*d, meanwhile the swing angle of load is less than10units

49、. 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 of fuzzy scheme. We uses the conventional method to control the crane for the purpose of comparison. When operating the crane according to

50、 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 impossible, 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 normali