起重机外文翻译

1、模糊控制式桥式起重机文摘:介绍了基于模糊控制的设计的吊车。而不是分析复杂非线性吊车系统,该方法使用简单但有效的方法来控制起重机。有双模糊控制器这处理反馈信息,位置的架空起重机和摆动角的负荷,抑制摇摆和加快速度当起重机运输沉重的负荷。这种方法简化了设计过程的起重机控制器;此外,双控制器方法减少了规则数当完成模糊系统。最后,实验结果通过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 介绍桥式吊车系统广泛应用于工业移动沉重的货物。因此反摇摆和位置控制已成为需求作为核心技术自动化起重机系统,能够灵活的空间自动运输。起重机控制的目的是减少的摆动负载移动时电车到所需的位置尽可能快的。然而,

2、高架起重机已经严重问题:起重机加速,需要运动,总是引起不良负荷摆动。这样的摇摆的负载通常会降低工作效率,有时导致负载损害赔偿,甚至安全事故。因此,需要更快货物装卸要求精确控制起重机的运动所以,它的动态性能得到了改进。传统上,起重机操作员驱动小车与这些步骤的加速运动,匀速运动,减速运动,运动和断裂图1显示了距离速度参考曲线的传统桥式吊车的操作4,5。有经验的起重机工人把电车精心保持负载从严重的摇摆。然而,保守的控制方法在现代工业无效。本研究提出了模糊双控制器来控制小车起重机。很多的尝试解决这个问题的摇摆的负载。他们中的大多数集中控制抑制负载摆动不考虑这个位置误差在起重机运动6。此外,几个作者考虑

3、优化技术来控制起重机。他们利用最小时间控制技术减少负载摆动(7 9)。因为负载的摆动取决于运动和加速度的小车,减少周期时间和减少负荷摆动部分相互冲突的需求。除此之外,还有许多论文调查稳定性问题的控制器设计10 12,但是这些研究缺乏实验说明了有效性。本研究提出了一种实用的解决方案为反摇摆和精确位置控制的起重机。这个职位的电车、旋角的负荷及其分化是应用于衍生出正确的控制输入的电车起重机。两个模糊逻辑控制器(FLC)是用来交易单独与反馈信号,偏转角和电车位置和他们的负效应。模糊规则是根据经验设计的起重机工人了主要利用这种分离方法是大大减少的计算复杂性起重机控制系统。模糊规则数的总完成控制系统因此不

4、到规则数量的常规模糊系统。此外,当设计提出的模糊控制器,没有数学模型系统是起重机提前要求。因此,该算法是非常容易被实现。本文组织如下。第二单元回顾了提出了模糊双控制器结构对于起重机控制系统。在第三单元,实验结果的起重机控制系统介绍了与传统的比较起重机控制方法来说明提出的优势模糊的方法。本文结尾总结第四单元。2 模糊逻辑控制器对起重机物理设备的高架起重机系统图在图2。桥式吊车的长度模型5米,高是2米。块图,这反映在图3,说明了提出了模糊逻辑起重机控制系统。在这个图中,两个编码器的分辨率2000 PPR(脉冲/圆)安装在起重机的小车检测运动位置和摆动角。反馈信号高架起重机作为输入变量的模糊控制器。

5、有两个类似的模糊逻辑控制器、位置控制器和摇摆控制器,分别与协议运动位置和旋角信息来驱动电车起重机。双模糊控制器作为像传统的pd型控制器。在设计中错误ep及其衍生物误差。ep选择输入语言变量的模糊位置控制器最初的小车位置设置为零,并在此基础上指数k意味着好听的样品时间。此外,输入语言变量的模糊控制器选用摇摆角e及其衍生角e>。左边的摆动载荷被定义为积极的摆动,而正确的摇摆的负荷负摇摆。后程序模糊模糊化、推理过程和去模糊化,一个表示输出语言变量的各自的位置控制器和swing控制器作为Up和u了。实际的动力来驱动小车被定义为u。设计程序都基于模糊位置控制器和摇摆控制器,描述了以下步骤13。步骤

6、1这一步证明输入信号模糊变量。输入和输出空间划分为若干个五个模糊区域互相重叠。一般来说,每个模糊区域标记的语言术语。这些语言条件输入变量的双控制器了问,NS,AZ,PS和pl一使用三角形和梯形隶属函数来证明输入语言变量。图4(a)(d)显示各自的隶属函数的 ep,ep,e和>e,分别获得从小车位置编码器和旋角编码器。这个范围的输入变量ep和ep是- d / 6,d / 6和-200200,e和。e是 (-40年,40和-40、40,分别和范围输出变量和u是5,5。语言输出变量方面,u被定义为五个模糊单例对象,这是代表视图(e)、控制伺服驱动的直流电机驱动小车起重机。步骤2这一步介绍了模糊

7、化函数对于每个输入变量来表达相关测量不确定度。一般来说,目的的模糊化函数f是解释测量输入变量,每个都是由一个实数表示,随着越来越多的现实的模糊近似在各自的数量提出了运用模糊单一功能模糊化过程。它意味着测量输入变量用于模糊推理引擎直接。步骤3为了实现模糊逻辑控制系统,既是位置控制器和摇摆控制器由二十5如果然后规则与下列形式A,B和C为模糊数的选择设置模糊数的表示语句中, NS,AZ,PS和PL,符号“*”(6)意味着p或。如果部分的模糊规则形成的误差和它的衍生物,其后果是决定根据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,总数量的可能都非冲突性的模糊规则位置控制器和摇摆控制器is2&

8、#215;52 = 50.了规则库是显示在表1和表。这些模糊规则可以很容易理解。假如设计师必须选择合适的推理和去模糊化方法来设计模糊控制器。推理和去模糊化程序转换结论模糊规则中获得一个真正的号码。由此产生的实数,在某种意义上,总结了弹性约束对可能的值输出变量的模糊集。对于每个输入单例对(e *和e *),一个计算他们的兼容性j程度(e *,e *)与前期的标定j.当每个推理j(e *,e *)> 0,j触发规则。至少一个规则解雇的原因是所有可能的输入对的模糊控制器设计。敏敏马克斯的推理方法用于总结所有的解雇规则在本文。为了获得期望当中的真正的价值,一个利用最常用的重心法将无法验证推理的结

9、果。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

10、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

11、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,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结论本文提供了基于双

12、控制器的模糊控制桥式吊车。应用该法,不仅运输速度增加但同样的摆动载荷是非常光滑。此外,提出的方法将输入语言变量进入两部分,位置变量和摆动变量因此,只有五十规则是必要的,以完成系统。这个提出了分离算法有助于减少计算复杂性的模糊控制器。实验结果表明,所提出的方法提高了模糊控制系统的性能桥式吊车。工作效率得以提高。1介绍移动液压机器必须面对的问题的低频振荡在行动,特别是在复杂的情况下。振荡阻尼液压系统实现了通过各种活动或高质量的控制技术1。由于操作简单,容易设计,便宜的维护、高成本效率,传统的PID控制,已被广泛应用于超过90情况下线性或准线性的工业过程系统2。然而,传统的PID(或PI,PD)控制

13、器不能令人满意地工作为高阶时变线性或非线性系统。许多优化PID参数有法律提出了满足一个特定的需求在过去几十年。尽管齐格勒尼科尔斯调优(ZNT)方法有一个好的能力的负载扰动衰减,它有一个吗可怜的阶段保证金。因此,PID与ZNT可能产生一个大过火和长期稳定时间在阶跃响应。因此,这种PID不能提供令人满意的控制性能。各种各样的模糊控制应用程序已经被发现全球3。基于语言代替传统数学模型、模糊逻辑的基础上设计专家的经验,决策和控制行为。模糊逻辑可以被添加到自动控制系统处理不仅线性也模糊系统控制的非线性。近年来,研究集成模糊逻辑与PID控制导致复合模糊PID(或模糊pi模糊pd控制4。组合形式的PID和模

14、糊逻辑是非常灵活的。为了提高提示阻尼的HCBS在工作期间,一个PI控制器基于FSPW下面发达。限制输出信号和防止风起,一个有限的半积分器(LSI)引入到集成的部分提出控制器5。通过使用一个摄像头和光电电子反馈控制范围,提示阻尼的HCBS可以人为调整。仿真结果验证了所提出的控制有效性策略。2模型的液压起重机臂系统由于机械的弱点HCBS,没有真实准确的提示位置可以达成,和一个相当低的阻尼振动系统中发生的。共振频率的液压和机械系统呆在一个狭窄的范围。这些导致振荡收到2005年11月17日,修订后的2006年6月19日。这项工作由湖南省自然科学基金(No.04JJ6033)和科研基金的湖南省教育部门(

15、没有。03 c066)。328 y杨et al。/杂志的控制理论和应用4(2006)327 - 330问题。通过使用一个光电范围相机,它是可能的来定位垂直位置的起重机提示和com -pensate弱者液压机械阻尼。这个版本提卡位移之间摄影机放在起重机提示和对象的位置是由光电测量范围相机。运动学的HCBS结构得到使用信号传感器在液压位置气缸和几何描述的HCBS。然后,逆运动学计算的HCBS确定伺服阀控制行动。一个HCBS显示在图1。3设计PI控制器基于FSPW以下基于FSPW PI控制器设计了后HCBS图3所示。提出的控制系统由一系列摄影机安装在夹的起重机,2个阀控PI控制器,包括一个比例前馈控

16、制器基于FSPW以下和大规模集成电路控制器。一个复杂的非线性系统通常是由不同类型的零件在不同的建模,开发担忧的ronments7。作为一个HCBS,其液压部件和结构建模,通过AMESIM DSH +,WINSIMU HOPSAN iti sim,或,而不同的控制算法在MATLAB或VISSIM实现。目前,许多专用的建模软件是仅由特定的环境。为了提高设计效率和质量,这些模型需要耦合和模拟。这将导致不同的联合仿真模型连接器可以独立工作或合作。在这里, MATLAB / SIMULINK和HOPSAN是用来做协同控制仿真。液压系统模型产生HOPSAN和控制仿真控制器设计时被做在MATLAB / SI

17、MULINK仿真。某些扩展,活塞杆的位置反映了控制精度,而动力性能HCBS映照出加速度的起重机建议包含信息在振荡级或阻尼以及响应速度。仿真结果无花果所示。4和5。在这两个数据,曲线1、2和3代表参考输入,活塞杆的位置和加速度的起重机提示,分别。参考输入到系统Xp2r = 0.26米。图4是结果在一个纯粹的位置比例反馈控制,而图5是,根据提出的PI控制。比较图5中的结果,这些视图,它可以被发现,提出的PI控制具有优越的能力使HCBS响应速度,同时保持一个好的阻尼。通过整合一个FSPW进PI控制器,液压起重机臂系统可以改善其以下设置点特征以及提示阻尼。这提高了HCBS在生产率和操作安全。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