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1、基于内模控制的模糊PID参数的整定摘要:在本文中将利用内模控制的整定方法实现模糊PID控制。此种控制方式首次应用于模糊PID控制 器，它包括一个线性PID控制器和非线性补偿部分。非线性补偿部分可视为一个干扰过程，模糊PID 控制器的参数可在分析的基础上确定内模结构。模糊PID控制系统利用李亚谱诺夫稳定性理论进行稳 定性分析。仿真结果表明利用内模控制整定模糊PID控制参数是有效的。1引言一般而言，传统的PID控制器对于十分复杂的被控对象控制效果不太理想，如高阶时滞系统。在 这种复杂的环境下，众所周知，模糊控制器由于其固有的鲁棒性可以有更好的表现，因此，在过去30 年中，模糊控制器，特别是，模糊P
2、ID控制器因其对于线性系统和非线性系统都能进行简单和有效的控 制，已被广泛用于工业生产过程妇。模糊PID控制器有多种形式珂,如单输入模糊PID控制器,双输 入模糊PID控制器和三个输入的模糊PID控制器。一般情况下，没有统一的标准。单输入可能会丢失 派生信息，三输入模糊PID控制器会产生按指数增长的规则。在本文中所采用的双输入模糊PID控制 器有一个适当的结构并且实用性强，因此在各种研究和应用中，是最流行的模糊PID类型。尽管业界 对于应用模糊PID有越来越大的兴趣，但从控制工程的主流社会的角度来看，它仍然是一个极具争议的 话题。原因之一是模糊PID参数整定的基本理论分析方法至今仍不明确。因此
3、，模糊PID控制器不得 不进行两个级别的整定。在较低层次上，该整定是由调整增益获得线性控制性能。在更高层次上的调 整，是由改变知识库参数以提高控制性能，然而调整知识库参数很难，此外，很难通过改变参数特性改 善瞬态响应。根据知识库传达一般控制规则倾向于保持成员函数不变，通过离线设计和调试工作扩大 增益，然而，由于由模糊PID控制器生成非线性控制表面的复杂性，调整机制的衡量因素和稳定性分 析仍然是艰巨的任务。如果非线性能得到适当的利用,模糊PID控制器可能得到比传统PID控制器更 好的系统性能。一些非常规的调整方法已进行了介绍W%虽然非线性被认为是在增益裕度和相位裕 度基础上获得的，但是由于非线性
4、因素，模糊PID控制器可能会产生比常规PID控制器较高的增益。 而高增益可能使控制系统的稳定性变差常规PID控制器很容易实现，大量的整定规则可以涵盖广泛的进程规格。在常规PID控制器的整 定方法中，内模控制基础整定是在商业PID控制软件包中流行的方法之一，因为只需调整一个参数， 便可以生产更好的设置点响应以。本文提出了一种基于内模控制的PID控制器的整定分析方法，模糊PID控制器可分解为线性PID 控制器加上非线性补偿部分的控制器。把非线性补偿部分近似看作一个过程干扰，模糊PID参数就可 以分析设计使用内模控制。模糊PID控制器的稳定性分析是根据李亚谱诺夫稳定性理论。最后，通过 仿真来证明此种
5、调整方法是有效的。Effective Tuning Method for Fuzzy PID with Internal Model ControlAn internal model control (IMC) based tuning method is proposed to auto tune the fuzzy proportional integral derivative (PID) controller in this paper. An analytical model of the fuzzy PID controller is first derived, which co
6、nsists of a linear PID controller and a nonlinear compensation item. The nonlinear compensation item can be considered as a process disturbance, and then parameters of the fuzzy PID controller can be analytically determined on the basis of the IMC structure. The stability of the fuzzy PID control sy
7、stem is analyzed using the Lyapunov stability theory. The simulation results demonstrate the effectiveness of the proposed tuning method.1. IntroductionGenerally speaking, conventional proportional integral derivative (PID) controllers may not perform well for the complex process, such as the high-o
8、rder and time delay systems. Under this complex environment, it is well-known that the fuzzy controller can have a better performance due to its inherent robustness. Thus, over the past three decades, fuzzy controllers, especially, fuzzy PID controllers have been widely used for industrial processes
9、 due to their heuristic natures associated with simplicity and effectiveness for both linear and nonlinear systems.1-4 There are too many variations of fuzzy PID controllers,such as, one-input, two-input, and three-input PID type fuzzy controllers. In general, there is no standard benchmark. The one
10、-input may miss more information on the derivative action, and the three-input fuzzy PID controllers may cause exponential growth of rules. The two-input fuzzy PID, as we used in the paper, has a proper structure and the most practical use, and thus is the most popular type of fuzzy PID used in vari
11、ous research and application. Despite the fact that industry shows greater and greater interest in the applications of fuzzy PID, it is still a highly controversial topic from the point of view of the mainstream control engineering community. One reason is that the fundamental theory for the analyti
12、cal tuning methods of fuzzy PID is still missing. Therefore, fuzzy PID controllers had to be tuned qualitatively by two-level tuning. At a lower level, the tuning is performed by adjusting the scaling gains to obtain overall linear control performance. At a higher level, the tuning is performed by c
13、hanging the knowledge base parameters to enhance the control performance. However, it is difficult to tune the knowledge base parameters. Moreover, it is hard to improve the transient response by changing the member fiinction.As the knowledge base conveys a general control policy, it is preferred to
14、 keep the member function unchanged and to leave the design and tuning exercises to scaling gains. However, the tuning mechanism of scaling factors and the stability analysis are still difficult tasks due to the complexity of the nonlinear control surface that is generated by fuzzy PID controllers.
15、If the nonlinearity can be suitably utilized, fuzzy PID controllers may pose the potential to achieve better system performance than conventional PID controllers. Some nonanalytical tuning methods were introduced.912 Although the nonlinearity was considered on the basis of gain margin and phase marg
16、in specifications, the fuzzy PID controller may produce higher gains than conventional PID controllers due to the nonlinear factor. A high gain could deteriorate the stability of the control system.15The conventional PID controller is easy to implement, and lots of tuning rules are available to cover a wide range of process specifications. Among tuning methods of the conventional PID controller, the internal model control (IMC) based tuning is one of the popular methods in
