用于载人飞船再入大气层航程控制的模糊控制器设计方法

本文将模糊控制理论应用于载人飞船再入大气层的落点航程控制问题，提出以纵程误差△DR、横程误差△CR、剩余时间tcf为输入变量，升力滚动角γν为输出变量，得到一个三输入单输出系统的模糊控制器并给出了便于工程应用的设计方法及仿真结果。     

一种新的模糊控制器的优化方法

通过分析对模糊控制器作优化的原理, 提出了一种新的优化设计方法. 用三个参数调整所定义的输入、输出语言变量的隶属函数, 通过单纯形法调整参数使性能指标最小, 从而使设计出的模糊控制器性能接近最优. 仿真结果表明, 本文的方法简单、有效, 对于模糊控制器的工程设计具有很大实用性.     

采用非线性规则和不均匀分布的输入输出隶属函数时典型模糊控制器的结构分析

针对两输入 (e,Δe)一输出 (Δu)的典型模糊控制器, 其输入变量采用三角形、全交迭、对称、不均匀分布的隶属函数, 输出变量采用对称、不均匀分布的单点隶属函数, 当采用非线性控制规则和Sum Product推理方法时, 推导了输出的解析表达式, 分析了其结构特性和极限特性, 证明了此类模糊控制器具有通用逼近性, 并讨论了典型模糊控制系统的局部稳定性.     

一种基于遗传算法的新的模糊控制器优化方法

通过分析对模糊控制器优化的原理,提出一种新的优化设计方法,通过引入等比因子,实现用三个参数调整输入、输出语言变量的隶属函数,再通过遗传算法寻优包括量化和比例因子在内的这些参数,使得性能指标最大,从而使设计出的模糊控制器性能更优。仿真结果表明,本文方法简单,有效。     

典型模糊控制器的解析表达式及其系统化设计方法

对两输入一输出的典型模糊控制器推导了其解析表达式,并对输入变量各取５个模糊子集的情况进行分析,提出一种设计模糊控制器的系统化方法,它能保证模糊控制器的性能在工作眯附近等效于ＰＩ控制器,而在远离工作点时明显优于ＰＩ控制器。仿真实验结果验证了该方法的有效性。     

基于模糊推理的模型参考自适应内模控制的研究

电厂的主汽温是大惯性、大滞后的时象,并且动态特性随机组负荷变化而变化,运行过程中扰动多.本文采用模型参考自适应控制的思想,将模糊控制方法和内模控制方法结合在一起,提出了一种模型参考模糊自适应内模控制方法:采用相消法设计内模控制器,用参考模型理想输出和实际对象输出之差e及其变化率△e在线模糊调节控制器中滤波参数.本文方法综合了模糊控制,内模控制和自适应控制的优点,增强了系统的鲁棒性,针对电厂热工过程系统的仿真研究结果证明了这一方法的有效性.     

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。     

基于增益调整型模糊PID的脉动真空灭菌器控制系统

针对目前脉动真空灭菌器控制效果不好而产生湿包的问题,结合Chien-Hrones-Reswicks整定算法,提出用模糊变增益PID的控制策略,控制器输入取控制舱内温度的偏差e和偏差变化率△e;根据实际操作经验,加入模糊规则,输出取PID控制器2个参数的调整值,从而实现PID参数的在线自整定;在MATLAB/SIMULINK环境下进行了仿真实验,进行了传统PID控制与模糊变增益PID控制动态性能的仿真比较,结果表明采用模糊变增益PID控制策略可明显提高脉动真空灭菌控制系统的动态性能.     

一类非线性离散系统的直接自适应模糊控制

针对一类含延迟非线性离散系统,提出了一种直接自适应模糊控制器设计的新方案．将系统用T-S模糊模型来表示,并基于并行分布补偿(PDC)基本思想设计了一种具有未知参数的模糊控制器,同时采用梯度下降算法对该控制器的参数进行在线辨识．通过输入到状态稳定(ISS)方法,证明了系统输出和参考输出的误差有界且满足一定的平均性能．仿真表明本方法的有效性．     

具有减少辨识参数的随机多变量极点配置自适应控制器

本文提出了一种新的限制输出个数减少随机多变量自适应控制中辨识参数的方法，并给出了减少辨识参数的极点配置自适应算法。虽然采用ｎ个输入１个输出的减少辨识参数的模型来设计控制器，但所提出的控制器能够保证被控系统的几个输出跟踪参考输入信号，仿真结果表明，所提出的方法是成功的。     

Developments of fuzzy PID controllers

Abstract: This paper describes the development and tuning methods for a novel self-organizing fuzzy proportional integral derivative (PID) controller. Before applying fuzzy logic, the PID gains are tuned using a conventional tuning method. At supervisory level, fuzzy logic readjusts the PID gains online. In the first tuning method, fuzzy logic at the supervisory level readjusts the three PID gains during the system operation. In the second tuning method, fuzzy logic only readjusts the proportional PID gain, and the corresponding integral and derivative gains are readjusted using the Ziegler–Nichols tuning method while the system is in operation. For the compositional rule of inferences in the fuzzy PID and the self-organizing fuzzy PID schemes two new approaches are introduced: the min implication function with the mean of maxima defuzzification method, and the max-product implication function with the centre of gravity defuzzification method. The fuzzy PID controller, the self-organizing fuzzy PID controller and the PID controller are all applied to a non-linear revolute-joint robot arm for step input and path tracking experiments using computer simulation. For the step input and path tracking experiments, the novel self-organizing fuzzy PID controller produces a better output response than the fuzzy PID controller; and in turn both controllers exhibit better process output than the PID controller.     

Output feedback controller design of Takagi–Sugeno fuzzy systems

This paper presents new systematic design methods of two types of output feedback controllers for Takagi–Sugeno (T–S) fuzzy systems, one of which is constructed with a fuzzy regulator and a fuzzy observer, while the other is an output direct feedback controller. In order to use the structural information in the rule base to decrease the conservatism of the stability analysis, the standard fuzzy partition (SFP) is employed to the premise variables of fuzzy systems. New stability conditions are obtained by relaxing the stability conditions derived in previous papers. The concept of parallel distributed compensation (PDC) is employed to design fuzzy regulators and fuzzy observers from the T–S fuzzy models. New stability analysis and design methods of output direct feedback controllers are also presented. The output feedback controllers design and simulation results for a nonlinear mass-spring-damper mechanical system show that these methods are effective.     

一种新型的二维PID模糊控制器

通过对常规PID控制器的结构分析,设计出一种新型的二维PID模糊控制器,其结构形式简称为fuzzy PD+ fuzzy ID型.根据模糊规则的图解分析,提出fuzzy ID控制嚣的输入变量(偏差和偏差变化加速率)与输出变量之间的控制结构,并确定两控制器的模糊控制规则的相似性.通过对该PID模糊控制器的结构分析,给出与常...     

基于增益协调方法的非线性控制器设计

为了寻求结构简单、易于进行稳定性分析的非线性控制器的设计方法,提出一种基于增益协调的非线性控制器设计方法理论。该控制器可以逼近很多复杂的非线性控制器,并具有相同的控制效果。该方法与模糊控制器的控制效果进行比较,说明了基于增益协调的非线性控制器简单、易于实现,同时更具便于调节的优点。同时基于李亚普诺夫稳定性理论,对例题中设计出的增益协调控制器的稳定性给予证明。结合桌飞控系统舵机控制实例,说明了该方法的实用性。     

基于LMI方法的T-S模糊系统的

研究一类T-S模糊系统的动态输出反馈耗散控制问题．给出了保证该系统耗散的动态输出反馈控制器的设计方法．动态输出反馈耗散控制器可通过求解一组线性矩阵不等式（LMI）获得．最后通过仿真例子说明了所提出的设计方法的有效性．     

Multi‐objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques

The present paper proposes a novel multi‐objective robust fuzzy fractional order proportional–integral–derivative (PID) controller design for nonlinear hydraulic turbine governing system (HTGS) by using evolutionary computation techniques. The fuzzy fractional order PID (FOPID) controller takes closed loop error and its fractional derivative as inputs and performs fuzzy logic operations. Then, it produces the output through the fractional order integrator. The predominant advantages of the proposed controller are its capability to handle complex nonlinear processes like HTGS in heuristic manner, due to fuzzy incorporation and extending an additional flexibility in tuning the order of fractional derivative/integral terms to enhance the closed loop performance. The present work formulates the optimal tuning problem of fuzzy FOPID controller for HTGS as a multi‐objective one instead of a traditional single‐objective one towards satisfying the conflicting criteria such as less settling time and minimum damped oscillations simultaneously to ensure the improved dynamic performance of HTGS. The multi‐objective evolutionary computation techniques such as non‐dominated sorting genetic algorithm‐II (NSGA‐II) and modified NSGA‐II have been utilized to find the optimal input/output scaling factors of the proposed controller along with the order of fractional derivative/integral terms for HTGS system under no load and load turbulence conditions. The performance of the proposed fuzzy FOPID controller is compared with PID and FOPID controllers. The simulations have been conducted to test the tracking capability and robust performance of HTGS during dynamic set point changes for a wide range of operating conditions and model parameter variations, respectively. The proposed robust fuzzy FOPID controller has ensured better fitness value and better time domain specifications than the PID and FOPID controllers, during optimization towards satisfying the conflicting objectives such as less settling time and minimum damped oscillations simultaneously, due to its special inheritance of fuzzy and FOPID properties.     

T-S模糊系统输出反馈控制器的稳定性分析与设计

输出反馈控制是T-S模糊控制系统设计的一种重要方法．本文提出了一类由模糊状态观测器和模糊调节器构成的输出反馈控制器稳定性分析和解析设计的新方法．为了减小稳定性分析的保守性和难度,本文充分利用了模糊规则前件变量模糊隶属度函数的结构信息,对前件变量采用标准模糊分划的T-S模糊系统输出反馈控制器进行了研究,获得了一些新的稳定性条件．然后采用平行分布补偿法(PDC)和线性矩阵不等式方法(LMI),研究了该类输出反馈控制器的解析设计方法．通过一个非线性质量块-弹簧-阻尼器系统输出反馈控制器的设计和计算机仿真,验证了本文方法的有效性．     

Adaptive hierarchical tuning of fuzzy controllers

Fuzzy controller design includes both linear and non-linear dynamic analysis. The knowledge base parameters associated within the fuzzy rule base influence the non-linear control dynamics while the linear parameters associated within the fuzzy output signal influence the overall control dynamics. For distinct identification of tuning levels, an equivalent linear controller output and a normalized non-linear controller output are defined. A linear proportional-integral-derivative (PID) controller analogy is used for determining the linear tuning parameters. Non-linear tuning is derived from the locally defined control properties in the non-linear fuzzy output. The non-linearity in the fuzzy output is then represented in a graphical form for achieving the necessary non-linear tuning. Three different tuning strategies are evaluated. The first strategy uses a genetic algorithm to simultaneously tune both linear and non-linear parameters. In the second strategy the non-linear parameters are initially selected on the basis of some desired non-linear control characteristics and the linear tuning is then performed using a trial and error approach. In the third method the linear tuning is initially performed off-line using an existing linear PID law and an adaptive non-linear tuning is then performed online in a hierarchical fashion. The control performance of each design is compared against its corresponding linear PID system. The controllers based on the first two design methods show superior performance when they are implemented on the estimated process system. However, in the presence of process uncertainties and external disturbances these controllers fail to perform any better than linear controllers. In the hierarchical control architecture, the non-linear fuzzy control method adapts to process uncertainties and disturbances to produce superior performance.