桥式起重机模糊控制外文翻译语序已改好

1、 中 北 大 学毕业设计外文资料翻译原文名称A practical fuzzy controllers scheme of overhead crane中文名称基于模糊控制式的桥式起重机原文来源：Chengyuan CHANG, Shihwei HSU, Kuohung CHIANG (Department of Electronic Engineering,ChingYun University,Jhongli,Taoyuan320,Taiwan,China)学生姓名：甘笛学号：1302014244学 院：机械与动力工程专 业：机械工程指导教师:王宗彦 2017 年 6 月 1 日基于模糊控

2、制式的桥式起重机摘要本文提出了一种基于模糊设计的桥式起重机控制设计。该方法是采用简单且有效的方式来控制起重机，而不是分析复杂非线性起重机系统。本次设计是用双模糊控制器的方法处理反馈信息。根据轨道起重机的位置和负载的摆动角度来压制摇摆，加快起重机运输时的速度。这种方式简化了起重机控制的设计程序；此外，当完成模糊系统时，双控制器的方法减少了指令数。最后，实验结果通过了起重机模型，证明了该方案的有效性。关键词:桥式起重机，模糊控制1 介绍桥式起重机系统在工业上广泛应用，用作搬运重物。因此，作为起重机自动化控制系统的核心技术，反摇摆和位置控制已成为一种必要条件,以保证能够灵活的进行空间上的自动化运行。

3、起重机控制的目的是降低摆动负载，尽可能在移动时，使小车快速的处于所需要到达的位置。然而,高架起重机已经出现了严重的问题：起重机的加速、所需要的运行动作,总是引起不良作用的负荷摆动。这样的摇摆负载通常会降低工作效率,有时会导致负载损耗,甚至造成安全事故。因此,为满足更快速装卸货物的要求，需要精确控制起重机的运行。所以,桥式起重机的动态性能得到了提升1-3。传统上,起重机的操作员通过加速运动、匀速运动、减速运动、周期运动和制动来驾驶轨道吊车。如图1所示，显示了传统桥式起重机的操作过程中，距离/速度关系的参考曲线4-5。有经验的起重机工人驾驶小车时，能小心地使负载免受剧烈摆动影响。然而，保守的控制方

4、法在现代工业中是没有作用的。本研究提出了用模糊双控制器来控制有轨吊运起重机。各种各样的尝试都是为了解决负载的摆动问题。其中的大多数都集中控制能力于负载摆动的抑制，而不考虑在起重机运行中的位置误差6。此外，还有一些作者考虑了通过优化技术来控制起重机。他们利用的控制技术，能以最少的时间来减少负载摆动7 - 9。因为负载的摆动取决于有轨吊车的运行及其加速度,最大限度的减少周期时间和减少负荷摆动是部分上相互矛盾的要求。除此之外,还存在许多研究控制器设计中稳定性问题的论文10-12,但是这些研究都缺少实验来说明其有效性。 本次研究提出了一种实用可行的解决方案，用于起重机的反摇摆和精确位置控制。有轨吊车的

5、位置、负载的摆动角度和它们之间的差异，被应用于获得有轨起重机的正确控制输入。两个模糊逻辑控制器(FLC)是用来分别处理反馈信号、摆动角度、有轨吊车的位置和他们之间的区别。这种模糊规则，是根据起重机工作人员的经验才设计出来的。这种分离方法的主要优点是，大大减少理论和应用(2005)起重机控制系统的计算复杂度。因此，实现控制系统的模糊规则数小于常规模糊系统的规则数量。此外，在设计模糊控制器的时候，不需要对起重机系统的数学模型进行数学建模。因此，提出来的这种算法是很容易实现的。本文组织如下。第二单元回顾了，被提出的基于起重机控制系统的模糊双控制器结构。在第三单元,通过对起重机控制系统的实验结果与传统

6、的起重机控制方法进行比较，说明了模糊方法的优点。本文在结尾处，总结了第四单元的内容。2 起重机上的模糊逻辑控制器桥式起重机系统的物理装置图，如图2所示。桥式起重机模型的长度是5 m，高度是2 m。如图3所示，其框图说明了，在本图中提出的模糊逻辑起重机控制系统，在这个图中,两个分辨率为2000 PPR(脉冲/圆)的编码器分别安装在起重机的轨道上，以探测运动位置和摆动角度。以桥式起重机的反馈信号作为模糊控制器的输入变量。有两种相似的模糊逻辑控制器，位置控制器和摆动控制器，分别对运动位置和摆动角度信息进行处理。双模糊控制器与传统的PD型控制器相似。在设计中，误差ep及其衍生误差，被选择为模糊位置控制

7、器的输入语言变量。在本文中，初始的有轨吊车位置设置为0。索引k表示第k个样本时间。此外,模糊控制器的输入语言变量，被选用为摆动角e及其衍生角E。负载的左摆动定义为正摆动，而负载的右摆动定义为负摆动。在模糊的程序模糊化后，推理过程和去模糊化,指示相应的位置控制器和摆动控制器的输出语言变量作为Up和u。驱动有轨吊车的实际力量被定义为u。下面的步骤介绍了基于模糊式的位置控制器和摆动控制器的设计过程13。步骤1:这一步将输入信号模糊化，得到模糊变量。输入和输出空间划分为五个模糊区域，互相重叠。一般来说,每个模糊区域都有一个特定的语言术语。这两个控制器的输入变量的语言术语分别为NL、NS、AZ、PS和P

8、L。是使用三角形和梯形之类的从属函数来使输入语言变量模糊化。如图4(a)(d)所示，表示的是ep、Ep、e和E各自的从属函数，分别是从有轨吊车的位置编码器和摆动角编码器中获得。输入变量ep和Ep的范围是- d / 6 d / 6和-200200,e和E的范围是-40,40和-40,40,输出变量Up和u的范围是-5,5。语言的输出变量up和u被定义为模糊化的单例5。在图4(e)中，控制DC_马达的伺服驱动器来驱动轨道桥式起重机。 步骤2：本步骤介绍了每个输入变量的模糊化函数，以表达相关的测量不确定性。一般来说，模糊化函数f的目的是解释输入变量的度量，每一个都是由一个实数表示的，这是相对于相应数

9、字的更现实的模糊近似。提出了模糊单例函数在模糊化过程中的应用。这意味着对输入变量的测量直接应用于模糊推理引擎。步骤3为了实现模糊逻辑控制系统,位置控制器和摆动控制器都是由二十五个IF-THEN指令集，如下式A,B和C为模糊数，在设置的模糊数的代表性学术语, NS、AZ、PS和PL中选择,其符号“*”(6)意味着p或。模糊规则的一部分是由误差及其衍生物构成的，其后果取决于起重机工人的经验和判断。因为每个输入变量有五个语言变量，其总数可能是非冲突的位置控制器和摆动控制器的模糊指令数之和，即有可能2×52 = 50。控制指令集，如表1和表2中所示。这些模糊式的指令可以很容易地理解。步骤4：

10、假设计者必须选择合适的推理和模糊化方法来设计模糊控制器。推理和去模糊化过程将从模糊规则获得的结论转换为一个实数。在某种意义上，产生的实数，总结了由模糊集对输出变量的可能值施加的弹性约束。对于每个输入单例对(e *和e *),计算他们的兼容性程度j(e *,e *)与判断的前提指令j.每当判断j(e *,e *)> 0,则指令j触发。在模糊控制器设计中，对所有可能的输入对至少有一条指令。利用最小的最小值推理方法来完成本文的所有触发指令。为了获得去模糊化的真实值，利用最常用的重心法来使判断结果去模糊化。输出的模糊位置和摇摆控制器和u上,分别。提出的双控制器结构提供了一种简便的但有效的方法来控

11、制模糊系统好。双控制器在本文单独的输入条件模糊规则分为两部分,位置和摆动角部分。因此,两个位置控制器和摇摆控制器只有米/ 2模糊先行词,每个包含N语言条款,那么必要的规则数量来满足该系统2 * NM / 2。规则数大大减少。对于例,既是位置控制器和摇摆控制器有两个输入语言变量。四个输入语言变量被划分成五个部分每个;因此必要的规则来控制起重机数量减少到50。与传统的模糊方案相比,分离双控制器方法有助于使模糊比平时更容易控制。此外,提出了双控制器结构适用于任何使用模糊控制应用程序。3 实验结果有几个实验结果说明基于模糊控制的优点起重机系统编码器的数据让我们知道真正的位置和摇摆的电车角的负载在任何时

12、间。手续后模糊化、模糊推理和去模糊化,每个模糊控制器得到控制的价值。作者使用求和的值的控制,u和来驱动电车。模糊控制器将控制电车到现有的距离的目标是更少的than0.01 * d,与此同时,旋角的负载是把单位少。这个符号d是初始距离的目标。使用2000 ppr编码器、一个单位等于360/2000的摇摆度。实验说明了模糊方案的优点。我们使用传统的方法来控制起重机比较的目的。当操作起重机根据速度参考曲线如所示图1、摩擦和限制机制将使精确的电车停在固定位置成为不可能的,因此额外的制动电车到目标是必要的。我们安装了了一个长为120cm的软线在这个实验。负载运输是3公斤了距离目标是39500归一化单元,

13、即。d =39500年。假设位置归一化单位车是0在一开始,图5(一个)显示位置的小车和负载的摆动角度、运送沉重的负荷传统的控制方法。一个可以很容易发现摇摆太严重破坏载荷图5(b)显示了结果模糊控制基础的方法。很明显,电车停在正确的位置和swing是微不足道的,与此同时,运输时间的负载是缩短了稳态误差是由于一些空气阻力造成的。当运输负载向目的地,这是更多的难以抑制的影响特别是传统方法然而,图5(b)表明,负载的影响模糊方法也很光滑。表演的解决起重机在定位和抑制的影响的负载比传统方案。4 结论本文为控制理论与应用(2005)266 - 270中269提供了基于模糊式双控制器控制的桥式起重机。通过应

14、用该方法，不仅提高了运输速度，而且载荷的摆动也非常平稳。此外，该方法将输入语言变量分为两部分，位置变量和swing变量。因此，只有50条规则才能实现这个系统。提出的离散算法有助于减少模糊控制器的计算复杂度。实验结果表明，该方法提高了桥式起重机的模糊控制系统的性能。因此，该设计提高了工作效率。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