基于输入整形的时滞系统最小拍控制

基于Ｎｙｑｕｉｓｔ准则,按照给定的相位稳定裕度设计ＰＩ控制器使一阶时滞对象稳定,然后引入输入整形控制器对输入进行整形,实现了一阶时滞对象的最小拍控制。在确定采用周期后,整个控制系统的设计尺需按相位稳定裕度的需求解析算出ＰＩ控制器的比例增益即可,设计简单,性能良好。仿真结果表明,该方法相当有效,实现的最小拍控制无纹波。     

基于信息融合的多输入模糊控制器设计方法

提出了一种基于信息融合的多输入模糊控制器设计方法,该方法首先利用信息融合技术完成输入变量维数的降低,然后利用降维后的输入变量设计常规模糊控制器,从而大大简化了多输入模糊控制器的设计过程。一级倒摆系统控制实例证明了该方法的可行性和鲁棒性。     

模糊控制器的结构化分析及系统化设计方法

对于模糊控制器的输入变量,采用一种新型的不均匀、全交迭、三角形的隶属度函数,推导了两输入（e,△e)－输出（△u）的典型模糊控制器输出的解析表达式,并对最常用的输入变量各取5个模糊变量的情况进行分析。在此基础上提出一种模糊控制系统的系统化设计方法,可根据已有的PI／PD控制器参数设计相应的模糊控制器参数。仿真实验说明了该方法的有效性。     

基于遗传算法的P—F—PI控制器人手臂定位控制中的应用

提出用遗传算法来优化控制参数的Ｐ－Ｆ－ＰＩ（比例－模糊－比例积分）控制器,控制机器人手臂定位系统。Ｐ－Ｆ－ＰＩ控制器是在大偏差时用比例控制,在中偏差时用Ｆ控制,接近稳态时用ＰＩ控制,而这三个控制器的切换参数以及Ｆ控制器中的修正系统用遗传算法来优化。实验证明,该控制方法能满足机器人手臂定位控制动态和静态要示求。     

Type-II型模糊控制器的结构分析与设计

全面研究了Type-II型模糊控制器的解析结构,推导出了单输入单输出和双输入单输出Type-II型模糊控制器的插值解析表达式,在一定程度上揭示了其本质特征。在此基础上,提出了Type-II型模糊控制器的一般设计步骤、优化方法和一种快速精确的控制算法。     

模糊控制器的实时精确算法与优化设计

将T-S型模糊控制器与PID控制器相结合,提出了TS-PID模糊控制器模型。推导出了输入采用正规模糊集、三角形全交迭隶属度函数的典型TS-PID模糊控制器的插值解析表达式,揭示了TS-PID模糊控制器本质上是一种非线性PID控制器,为实际应用提供了一种快速精确的控制算法。基于该插值表达式,进一步探讨了利用遗传算法对TS-PID模糊控制器进行优化设计的方法。     

多输入多输出系统的一种鲁棒PI控制器设计

本文在线性系统的基础上针对多输入多输出系统提出了一种鲁棒ＰＩ控制器设计方案，该控制器将滑模变结构控制和ＰＩ调节器的优点有机地结合起来。本文给出的控制方案对多输入事输出线性系统和特殊形式的非线性系统是适合的。理论分析和仿真结果表明该方案能使复杂的受控对象具有良好的性能鲁棒性和稳定鲁棒性，能消除各种持续干扰对闭环系统平衡点的影响和消除抖动现象。控制器设计简单，适合于高性能的复杂控制系统设计。     

无氧铜杆炉温前馈-反馈模糊控制

以无氧铜杆炉温度控制为例,介绍利用前馈－反馈模糊控制器代替一般控制器,即设计一种复合功能模糊控制器方法;其反馈部分是二输入单输出模糊控制器,前馈部分是以电网电压变化为前馈输入,通过模糊控制算法得到前馈控制量;该方法改善了控制性能。     

Type-Ⅱ型模糊控制器的结构分析与设计

全面研究了Type-Ⅱ型模糊控制器的解析结构,推导出了单输入单输出和双输入单输出Type-Ⅱ型模糊控制器的插值解析表达式,在一定程度上揭示了其本质特征.在此基础上,提出了Type-Ⅱ型模糊控制器的一般设计步骤、优化方法和一种快速精确的控制算法.     

一种典型二阶系统的模糊控制器的设计

介绍一种典型二阶系统的模糊控制器的设计方法。按照常规的步骤设计模糊控制器,在论域定义时采用一种新方法：根据未加入模糊控制器时,对应于系统模糊控制器输入、输出各个超调量的大小来设定模糊控制器的论域。实验仿真结果表明该设计方法能使系统达到较好的控制效果。     

A general technique for deriving analytical structure of fuzzy controllers using arbitrary trapezoidal input fuzzy sets and Zadeh AND operator

Deriving the analytical structure of fuzzy controllers is very important as it creates a solid foundation for better understanding, insightful analysis, and more effective design of fuzzy control systems. We previously developed a technique for deriving the analytical structure of the fuzzy controllers that use Zadeh fuzzy AND operator and the symmetric, identical trapezoidal or triangular input fuzzy sets. Many fuzzy controllers use arbitrary trapezoidal/triangular input fuzzy sets that are asymmetric. At present, there exists no technique capable of deriving the analytical structure of these fuzzy controllers. Extending our original technique, we now present a novel method that can accomplish rigorously the structure derivation for any fuzzy controller, Mamdani type or TS type, that employs the arbitrary trapezoidal input fuzzy sets and Zadeh fuzzy AND operator. The new technique contains our original technique as a special case. Given the importance of PID control, we focus on Mamdani fuzzy PI and PD controllers in this paper and show in detail how to use the new technique for different configurations of the fuzzy PI/PD controllers. The controllers use two arbitrary trapezoidal fuzzy sets for each input variable, four arbitrary singleton output fuzzy sets, four fuzzy rules, Zadeh fuzzy AND operator, and the centroid defuzzifier. This configuration is more general and complicated than the Mamdani fuzzy PI/PD controllers in the current literature. It actually contains them as special cases. We call this configuration the generalized fuzzy PI/PD controller.     

一种解析的模糊PI自整定方法

针对模糊PID控制器缺乏系统的整定方法的问题,提出了一种解析的基于增益裕度和相位裕度的模糊PI控制器的参数自整定方法。首先推导出模糊PI控制器的解析模型,该解析模型包括线性控制器和非线性补偿控制器2个部分。参数整定时,将非线性补偿控制器看作过程的扰动,由线性控制器和被控对象的一阶纯时滞模型,基于系统的增益裕度和相位裕度,导出模糊PI控制器的参数。仿真结果表明,对于时变高阶系统,和传统的PI控制器相比,模糊PI控制器具有鲁棒性强,超调小,调整时间短等优点。     

Structural analysis of fuzzy controllers with nonlinear input fuzzy sets in relation to nonlinear PID control with variable gains

The popular linear PID controller is mostly effective for linear or nearly linear control problems. Nonlinear PID controllers, however, are needed in order to satisfactorily control (highly) nonlinear plants, time-varying plants, or plants with significant time delay. This paper extends our previous papers in which we show rigorously that some fuzzy controllers are actually nonlinear PI, PD, and PID controllers with variable gains that can outperform their linear counterparts. In the present paper, we study the analytical structure of an important class of two- and three-dimensional fuzzy controllers. We link the entire class, as opposed to one controller at a time, to nonlinear PI, PD, and PID controllers with variable gains by establishing the conditions for the former to structurally become the latter. Unlike the results in the literature, which are exclusively for the fuzzy controllers using linear fuzzy sets for the input variables, this class of fuzzy controllers employs nonlinear input fuzzy sets of arbitrary types. Our structural results are thus more general and contain the existing ones as special cases. Two concrete examples are provided to illustrate the usefulness of the new results.     

Design and analysis of region-wise linear fuzzy controllers

In this paper, a methodology to reduce the complexity of a robust controller based on fuzzy if-then rules is proposed. The motivation and the design of this complexity-reduced fuzzy controller are presented. This fuzzy controller with the triangular membership functions and fuzzy partition methods used here leads to a region-wise linear fuzzy controller (RLFC). The properties of the region-wise linear fuzzy controllers are discussed and the reasons why they in general perform better than the PD controllers are also provided. And the simulation results based on a second order plant are included to show that the region-wise linear fuzzy controller outperforms the PD controller. We also show that the region-wise linear fuzzy controller and original fuzzy controller have similar performances.     

Robust PI controller design for nonlinear systems via fuzzymodeling approach

The design problem of proportional and proportional-plus-integral (PI) controllers for nonlinear systems is studied. First, the Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties is used to approximate the nonlinear systems. Then a numerically tractable algorithm based on the technique of iterative linear matrix inequalities is developed to design a proportional (static output feedback) controller for the robust stabilization of the system in T-S fuzzy model. Next, we transform the problem of PI controller design to that of proportional controller design for an augmented system and thus bring the solution of the former problem into the configuration of the developed algorithm. Finally, the proposed method is applied to the design of robust stabilizing controllers for the excitation control of power systems. Simulation results show that the transient stability can be improved by using a fuzzy PI controller when large faults appear in the system, compared to the conventional PI controller designed by using linearization method around the steady state     

Fuzzy controllers: synthesis and equivalences

It has been proved that fuzzy controllers are capable of approximating any real continuous control function on a compact set to arbitrary accuracy. In particular, any given linear control can be achieved with a fuzzy controller for a given accuracy. The aim of this paper is to show how to automatically build this fuzzy controller. The proposed design methodology is detailed for the synthesis of a Sugeno or Mamdani type fuzzy controller precisely equivalent to a given PI controller. The main idea is to equate the output of the fuzzy controller with the output of the PI controller at some particular input values, called modal values. The rule base and the distribution of the membership functions can thus be deduced. The analytic expression of the output of the generated fuzzy controller is then established. For Sugeno-type fuzzy controllers, precise equivalence is directly obtained. For Mamdani-type fuzzy controllers, the defuzzification strategy and the inference operators have to be correctly chosen to provide linear interpolation between modal values. The usual inference operators satisfying the linearity requirement when using the center of gravity defuzzification method are proposed     

采用新的DNA进化算法自动设计Takagi-Sugeno模糊控制器

提出一种新颖的基于DNA的进化算法(DNA-EA)来自动设计一类Trakagi-Sugeno (TS)模糊控制器.TS模糊控制器采用带有线性规则后项的TS模糊规则,连续输 入模糊集,Zadeh模糊逻辑和常用的重心反模糊器.TS模糊控制器被证明是带有可变增 益的非线性PI控制器.DNA-EA被用于自动获取TS模糊规则,并同时优化模糊规则前 项和后项中的设计参数.DNA-EA采用由生物DNA结构启发得到的DNA编码方法来编 码模糊控制器的设计参数.在DNA-EA中,引入了受微生物进化现象启发的基因转移和细 菌变异操作.另外,也引入了基于DNA遗传操作的框构变异操作.DNA编码方法非常适 合于复杂知识的表达,基于基因水平的遗传操作也很容易引入到DNA-EA中.染色体的长 度是可变的,且可插入或删除部分碱基序列.作为示例,给出了采用DNA-EA来自动设计 TS模糊控制器用于控制一类非线性系统的方法.DNA-EA能自动地构造模糊控制器.计 算机仿真结果表明,DNA-EA是有效的,且优化得到的模糊控制器是满意的.     

Analysis of direct action fuzzy PID controller structures

The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.     

The simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral controllers with variable gains

The author analytically proves that the simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral (PI) controllers with proportional-gains and integral-gains changing with inputs of the controllers. The inference methods involved are Mamdani's minimum inference method, Larsen's product inference method, the drastic product inference method and the bounded product inference method. Configuration of the fuzzy controllers is minimal, which includes two input fuzzy sets, three output fuzzy sets, four control rules, Zadeh fuzzy logic AND, Lukasiewicz fuzzy logic OR and a center of gravity defuzzification algorithm. After analytically investigating properties of the nonlinear PI controllers, the author reveals that the bounded product inference method is inappropriate for the control purpose while the other three inference methods are appropriate. Dynamic and static control behaviors of the fuzzy controllers with the appropriate inference methods are analytically compared with each other, and are also compared with those of the linear PI controller. Finally, it is analytically proven that the fuzzy control systems have the same local stability at the equilibrium point as the corresponding linear PI control system does.