毕业设计外文参考资料及译文封面

1、毕业设计（论文）外文参考资料及译文译文题目：Inverted Pendulum学生姓名：徐飞马学 号：0704111030专业：07自动化所在学院：机电学院指导教师：陈丽换职称：讲师2011年2月24日The inverted pendulumKey words: inverted pendulum, modeling, PID controllers,LQRcontrollersWhat is an Inverted Pendulum? Remember when you were a child and you tried to balance a broom-stick or baseb

2、all bat on your index finger or the palm of your hand? You had to constantly adjust the position of your hand to keep the object upright. An Inverted Pendulum does basically the same thing. However, it is limited in that it only moves in one dimension, while your hand could move up, down, sideways,

3、etc. Che ck out the video provided to see exactly how the Inverted Pendulum works.An inverted pendulum is a physical device consisting in a cylindrical bar (usually o f aluminum) free to oscillate around a fixed pivot. The pivot is mounted on a carriage, which in its turn can move on a horizontal di

4、rection. The carriage is driven by a moto r, which can exert on it a variable force. The bar would naturally tend to fall down fro m the top vertical position, which is a position of unsteady equilibrium.The goal of the experiment is to stabilize the pendulum (bar) on the top vertical pos ition. Thi

5、s is possible by exerting on the carriage through the motor a force which ten ds to contrast the 'free' pendulum dynamics. The correct force has to be calculated me asuring the instant values of the horizontal position and the pendulum angle (obtained e.g. through two potentiometers).The sys

6、tem pendulum+cart+motor can be modeled as a linear system if all the para meters are known (masses, lengths, etc.), in order to find a controller to stabilize it. If not all the parameters are known, one can however try to 'reconstruct' the system para meters using measured data on the dynam

7、ics of the pendulum.The inverted pendulum is a traditional example (neither difficult nor trivial) of a co ntrolled system. Thus it is used in simulations and experiments to show the performan ce of different controllers (e.g. PID controllers, state space controllers, fuzzy controlle rs.).The Real-T

8、ime Inverted Pendulum is used as a benchmark, to test the validity and the performance of the software underlying the state-space controller algorithm, i.e. th e used operating system. Actually the algorithm is implement form the numerical poin t of view as a set of mutually co-operating tasks, whic

9、h are periodically activated by t he kernel, and which perform different calculations. The way how these tasks are acti vated (e.g. the activation order) is called scheduling of the tasks. It is obvious that a co rrect scheduling of each task is crucial for a good performance of the controller, and

10、h ence for an effective pendulum stabilization. Thus the inverted pendulum is very usef ul in determining whether a particular scheduling choice is better than another one, in which cases, to which extent, and so on.Modeling an inverted pendulum.Generally the inverted pendulum system is modele d as

11、a linear system, and hence the modeling is valid only for small oscillations of the pendulum.With the use of trapezoidal input membership functions and appropriate compositio n and inference methods, it will be shown that it is possible to obtain rule membership functions which are region-wise affin

12、e functions of the controller input variable. We propose a linear defuzzification algorithm that keeps this region-wise affine structure and yields a piece-wise affine controller. A particular and systematic parameter tuning method will be given which allows turning this controller into a variable s

13、tructure-like controller. We will compare this region-wise affine controller with a Fuzzy and Varia ble Structure Controller through the application to an inverted pendulum control.We will begin with system design; analyzing control behavior of a two-stage invert ed pendulum. We will then show how t

14、o design a fuzzy controller for the system. We will describe a control curve and how it differs from that of conventional controllers when using a fuzzy controller. Finally, we will discuss how to use this curve to define labels and membership functions for variables, as well as how to create rules

15、for the c ontroller.In the formulation of any control problem there will typically be discrepancies betw een the actual plant and the mathematical model developed for controller design.This mismatch may be due to unmodelled dynamics, variation in system parameters or the approximation of complex pla

16、nt behavior by a straightforward model.The engineermust ensure that the resulting controller has the ability to produce the required perfor mance levels in practice despite such plant/model mismatches. This has led to an inte nse interest in the development of so-called robust control methods which

17、seek to solv e this problem. One particular approach to robust control controller design is the so-ca lled sliding mode control methodology.The Inverted Pendulum is one of the most important classical problems of Control Engineering.Broom Balancing (Inverted Pendulum on a cart) is a well known examp

18、l e of nonlinear, unstable control problem. This problem becomes further complicated when a flexible broom, in place of a rigid broom, is employed. Degree of complexity and difficulty in its control increases with its flexibility. This problem has been a rese arch interest of control engineers.Contr

19、ol of Inverted Pendulum is a Control Engineering project based on the FLIG HT SIMULATION OF ROCKET OR MISSILE DURING THE INITIAL STAGES OF FLIGHT. The AIM OF THIS STUDY is to stabilize the Inverted Pendulum such tha t the position of the carriage on the track is controlled quickly and accurately so

20、that t he pendulum is always erected in its inverted position during such movements.This practical exercise is a presentation of the analysis and practical implementatio n of the results of the solutions presented in the papers,“ RobustController for Nonlin ear & Unstable System: InvertedPendulu

21、m”and “ FlexibleBroom Balancing ”,in whi ch this complex problem was analyzed and a simple yet effective solution was pre sented.Prescribed trajectory tracking with certain accuracy is a main task of robotic contro l. The control is often based on a mathematical model of the system. This model is ne

22、 ver an exact representation of reality, since modeling errors are inevitable. Moreover, one can use a simplified model on purpose. In this paper, the structured and unstructur ed uncertainties are of primary interest, i.e., the modeling error due to the parameters variation and unmodeled modes, esp

23、ecially the friction and sensor dynamics, neglecte d time delays,The erroneous model and the demand for high performance require the controller to be robust. The sliding mode controllers(SMC) based on variable structure control can be used if the inaccuracies in the model structure are bounded with

24、known bounds. However, an SMC has some disadvantages, related to chattering of the control input si gnal. Often this phenomenon is undesirable, since it causes excessive control action le ading to increase wear of the actuators and to excitation of unmodeled dynamics.The attempts to attenuate this u

25、ndesirable effect result in the deterioration of the ro bustness characteristics. This is a well-known problem and widely treated in the literat ure. In order to obtain smoothing in the bang-bang typed discontinuities of the sliding mode controller different schemes have been suggested.Another impor

26、tant issue limiting the practical applicability of SMC is the over cons ervative control law due to the upper bounds of the uncertainties. In practice most ofte n the worst case implemented in control law does not take place and the resulting larg e control inputs become unnecessary and uneconomical

27、.In this paper we suggest an approach to the design of decentralized motion controll ers for electromechanical systems besides the sliding mode motion controller structure and disturbance torque estimation. The accuracy of the estimation is the critical para meter for robustness in this scheme, as o

28、pposed to the upper bounds of the perturbatio ns themselves. Consequently, the driving terms of the error dynamics are reduced fro m the uncertainties (as in the conventional SMC) to the accuracy in their estimates. T he result is a much better tracking accuracy without being over conservative in co

29、ntrol .Experimental robustness properties of fuzzy controllers remain theoretically difficu lt to prove and their synthesis is still an open problem. The non-linear structure of the final controller is derived from all controllers at the different stages of fuzzy control, p articularly from common d

30、efuzzification methods (such as Centre of Area). In general, fuzzy controllers have a region-wise structure given the partition of its input space b y the fuzzification stage. Local controls designed in these regions are then combined i nto sets to make up the final global control. A partition of th

31、e state space can be found for which the controller has region-wise constant parameters. Moreover, each fuzzy c ontroller tuning parameter (i.e. the shapes and the values of input or output variables membership functions) influences the values of parameters in several regions at the same time. In th

32、e particular case of a switching line separating the phase plane into one region where the control is positive whereas in the other it is negative, the fuzzy contr oller may be seen as a variable structure controller. This kind of a fuzzy controller can be assimilated to a variable structure control

33、ler with boundary layer such as in, for w hich stability theorems exist, but with a non-linear switching surface.We will begin with system design; analyzing control behavior of a two-stage inverted pendulum. We will then show how to design a fuzzy controller for the system. We will describe a contro

34、l curve and how it differs from that of conventional controllers when using a fuzzy controller. Finally, we will discuss how to use this curve to define label s and membership functions for variables, as well as how to create rules for the contro ller.In the formulation of any control problem there

35、will typically be discrepancies betw een the actual plant and the mathematical model developed for controller design.This mismatch may be due to unmodelled dynamics, variation in system parameters or the approximation of complex plant behavior by a straightforward model.The engineer must ensure that

36、 the resulting controller has the ability to produce the required perfor mance levels in practice despite such plant/model mismatches. This has led to an inte nse interest in the development of so-called robust control methods which seek to solv e this problem. One particular approach to robust cont

37、rol controller design is the so-ca lled sliding mode control methodology.Sliding mode control is a particular type of Variable Structure Control System (VS CS). A VSCS is characterized by a suite of feedback control laws and a decision rule. The decision rule, termed the switching function, has as i

38、ts input some measure of the current system behavior and produces as an output the particular feedback controller which should be used at that instant in time. A variable structure system,which may be regarded as a combination of subsystems where each subsystem has a fixed control s tructure and is

39、valid for specified regions of system behavior, results. One of the adva ntages of introducing this additional complexity into the system is the ability to combi ne useful properties of each of the composite structures of the system. Furthermore, the system may be designed to possess new properties

40、not present in any of the composi te structures alone. Utilization of these natural ideas began in the Soviet Union in the l ate 1950's.In sliding mode control, the VSCS is designed to drive and then constrain the syste m state to lie within a neighborhood of the switching function. There are tw

41、o main ad vantages to this approach. Firstly, the dynamic behavior of the system may be tailored by the particular choice of switching function. Secondly, the closed-loop response be comes totally insensitive to a particular class of uncertainty. The latter invariance prop erty clearly makes the met

42、hodology an appropriate candidate for robust control. In ad dition, the ability to specify performance directly makes sliding mode control attractiv e from the design perspective.The sliding mode design approach consists of two components. The first involves t he design of a switching function so th

43、at the sliding motion satisfies design specificati ons. The second is concerned with the selection of a control law which will make the switching function attractive to the system state. Note that this control law is not nece ssarily discontinuous.We will provide the reader with a thorough grounding

44、 in the sliding mode control a rea and as such is appropriate for the graduate with a basic knowledge of classical con trol theory and some knowledge of state-space methods. From this basis, more advanc ed theoretical results are developed. Resulting design procedures are emphasized usin g Matlab fi

45、les. Fully worked design examples are an additional tutorial feature. Indust rial case studies, which present the results of sliding mode controller implementations, are used to illustrate the successful practical application of the theory.倒立摆关键词：倒立摆，模型，PID 控制， LQR 控制倒立摆是什么？还记得当你是个孩子时你曾用你的食指或者掌心设法去平

46、衡一把扫帚柄或者棒球棍吗？你必须不断地调整你的手的位置以保持对象的垂直。一个倒立摆在本质上就是做相同的事情。然而，它会受限制因为它只能在一 定范围内移动，虽然你的手可以上升、下降、斜向一边等等。检查录象提供的画面来观察倒立摆是如何确切地工作的。一个倒立摆是个物理设备它包括一个圆柱体的棒子 （通常是铝的） 可以在一个支点周围振荡。 这个支点是安在一个车架上， 它的转动方向是水平的偏转。 小车是由一个马达控制的， 它可以运用于一个变力。 棒子会有自然的趋势从最高的竖直位置下落，那是一个不稳定的平衡位置。实验的目标是使摆 （棒子）稳定在最高的竖直位置。 这是有可能的只要运用通过马达的小车一个力该力可

47、以与“自由”摆的动力学抵消。 这个正确的力必须通过计算测量水平偏转的瞬时值和摆的角度（获得两个电位计） 。倒立摆是干什么的？就好象扫帚柄，一个倒立摆是一个天生的不稳定系统。力度必须被严格地应用以保持系统的完整性。 为了实现它，严格的控制理论是必须的。倒立摆在求数值和各种控制理论的比较中是必要的。倒立摆是一个控制器系统中的一个传统的例子 （既不困难也不是没有价值） 。尽管它是仿真和实验来显示不同控制器的性能 （举例来说 PID 控制器，状态空间控制器，模糊控制器）。实时倒立摆被作为一个基准， 去测试软件在状态空间控制器运算法则下的有效性和性能，也就是实用的操作系统。 事实上运算法则是通过数值点实

48、现的该数值点看作一组互助的协同操作的任务， 它是周期性的通过核心的活动， 它执行不同的计算。这些任务如何活动的方法 （举例来说激活命令） 被称作任务的时序安排。很明显每个任务的时序安排对控制器的一个好的性能是至关紧要的， 因此对一个摆的稳定性是有效的。 如此倒立摆是非常有用的在决定是否一个特殊的时序安排的选择比另一个好，在哪个情形下，在什么程度内等等。为倒立摆建模。 通常倒立摆系统建模成一个线形系统， 因此模型只对小幅度摆动的摆才有效。通过梯形输入隶属函数的使用和适当的作图法和推论方法， 这将说明那是有可能遵循规则区域劝导的输 入变量仿射函数的隶属函数。我们提出线形逆模糊化算法它能这个区域劝导

49、仿射结构和产生一个块仿射控制 器。一个特殊的系统的参数调节方法将会被给定它允许把这个控制器调节成一个可变的结构相似的控制器。 我们将比较这个区域劝导仿射控制器和一个模糊的可变结构的控制器通过应用一个倒立摆控制。我们将从系统设计开始； 分析二级倒立摆的控制行为。 随后我们将展示如何为系统设计一个模糊控制装置。我们将描绘一个控制曲线当使用模糊控制装置时它与一个常规控制器是如何的不同。最后，我们将讨论如何使用这个曲线去定义标志还有变量的隶属函数，还有就是如何为控制器创立一套规则。“倒立摆、分析、设计和执行”是由一个MATLAB方程和内容的收藏的，还有 SIMULINK模型，对分析倒立摆系统和设计控制

50、系统是很有用的。这个报道 MATLAB 文件收藏是由少量的控制系统分析的实际任务而发展的，设计和发展实际问题。这分派 的倒立摆的问题是一个控制系统的实验室工作的一部分。倒立摆是最重要最经典的控制工程问题中的一个。帚平衡（车载的倒立摆）是一个著名的非线形例子， 不稳定的控制问题。这个问题越来越复杂当一个柔韧的帚代替一个刚硬的帚被使用。复杂的问题的真实度和 难度在控制中随着弹性而增长。这个问题已经引起调度工程师的兴趣并展开研究。倒立摆的控制是一个控制工程的方案基于火箭的飞行模拟或者导弹飞行的初始状态。这个学习的目的是 稳定倒立摆这样小车的位置在轨道上被控制得快速和准确以使摆在这一装置下始终垂直在它

51、的倒立位置。这个实际的运动是一个分析的表现还有实际的执行在解决问题的结果中在本文中，“非线形和不稳定系 统的坚固的控制器：倒立摆”和“柔韧的帚平衡”，其中这个复杂问题分析和一个简单的有效的解决方案被引出法定轨道通过确定的精确性是机器控制的一个主要任务。 控制通常是基于一个系统的数学模型。模型不是一 个准确的实体表现，模型的误差是不可避免的。此外，我们可以特意使用一个简化的模型。 在这篇论文中， 构造好的和未构造好的不确定因素是主要的兴趣所在， 也就是说模型的误差导致参数变化和未模型化的模式 ，尤其是摩擦力和敏感元件的力度，被忽视的时间延迟等等。不正确的模型和高性能的需求要求控制器非常坚固。滑模控制器 (SMC)是基于变结构控制使用的如果模型结构中的错误在已知的范围内跃进。然而，一个SMC有一些缺点，涉及控制输入信号的振动。通常这个现象是令人不快的，它会引起额外的控制作用从而导致激励者穿戴的增加和未建模动力学的刺激。削弱这个令人不快的效果的尝试导致