一种模糊控制器的实现方法

介绍用硬件电路实现模糊控制器的方法。结合实例，具体介绍了如何用硬件电路实现输入模糊化、模糊推理和逆模糊化的方法。     

变论域模糊控制器伸缩因子的选择方法

对变论域模糊控制器伸缩因子选取的易实现性和输出的实时性问题进行了讨论,给出了一种选择伸缩因子的新方法.该方法与积分型伸缩因子和指数型伸缩因子选择相比具有计算量小、参数少的特点.通过仿真给出了该伸缩因子参数选择的原则.结合间歇反应釜工程应用,将此伸缩因子设置在变论域模糊控制器模糊推理的前件和后件,验证了此伸缩因子的有效性:在保证高精度的基础上,输出能快速跟踪系统的输入,为变论域模糊控制器实际应用提供了一种有效的解决方案.     

模糊蕴含运算和模糊推理研究

1 引言模糊推理是一种利用模糊集合论研究不确定性问题的方法。实际上多输入多输出系统的推理问题的关键是单输入单输出系统的模糊推理问题,因此本文只考虑单输入单输出系统的模糊推理问题。模糊推理中涉及到两个主要的问题:一是模糊蕴涵算子的选择,二是推理合成方式的选择。模糊蕴涵算子将规则转换成     

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。     

基于分变量模糊蕴涵关系的无规则模糊逻辑系统的自适应控制应用

常用的基于模糊if-then规则的模糊逻辑系统在进行控制设计应用时常会遇到规则爆炸问题,为此,提出了一种新的无规则模糊逻辑系统。首先利用从属于系统各分变量论域上开覆盖的单位分解,将模糊知识库中的专家模糊语言值信息进行整合,得到相应的分变量模糊蕴涵关系,再由模糊推理及解模糊化得到系统输出;然后,针对一类非线性不确定系统,先利用伸缩器和饱和器构造出扩展的无规则模糊逻辑系统,再设计被控系统的自适应稳定控制器;最后通过仿真算例验证了该方法的有效性。这种方法的优越性在于新的模糊逻辑系统的构造与自适应律的设计是分离的,在线估计的参数数目明显减少,有效地解决了规则爆炸问题。     

一种变论域模糊控制器伸缩因子选择的新方法

对变论域模糊控制器伸缩因子选取的易实现性和输出的实时性问题进行了讨论,给出了一种选择伸缩因子的新方法。该方法与积分型伸缩因子和指数型伸缩因子选择相比具有计算量小、参数少的特点。通过仿真给出了该伸缩因子参数选择的原则。结合间歇反应釜工程应用,将此伸缩因子设置在变论域模糊控制器模糊推理的前件和后件,验证了此伸缩因子的有效性：在保证高精度的基础上,输出能快速跟踪系统的输入,为变论域模糊控制器实际应用提供了一种有效的解决方案。     

基于模糊神经网络的彩色图像滤波方法

提出一种基于模糊神经网络的彩色图像滤波方法．该方法将滤波窗口内的像素矢量作为模糊神经网络的输入，根据像素间的矢量距离进行模糊化，通过模糊推理实现对各个像素加权求均值，得到中心像素的输出．输入的模糊化和模糊推理参数由神经网络的自学习功能自动调整，实现最优的滤波效果．对样本图像的处理结果表明，该滤波方法对不同类型的噪声均有较好的滤波效果。     

模糊神经网络在非线性短期负荷预测中的应用

为提高负荷预测精度,提出了一种新的4层模糊神经网络短期负荷预测模型.该模型将模糊逻辑和神经网络的长处融合在一起,使模糊推理和解模糊均通过神经网络来实现.选取的隶属函数使神经网络权值有一定的知识表示意义,并通过模糊化层将输入特征量转化为模糊量.在模糊推理层提出了两种不同的算法来完成模糊推理,然后从中确定出模糊取小算法预测效果更好.最后在输出层通过适当的解模糊得到确切的预测输出值.仿真结果表明了该方法的有效性.     

pH值系统变论域模糊控制器的设计及性能分析

针对pH值非线性控制系统,设计了一种实时简化变论域模糊控制器.将变论域思想与实时模糊推理策略相结合:一方面论域随着误差的减小而收缩,而论域的收缩相当于控制规则的增加,从而增加了控制精度;另一方面采用实时模糊推理方法,即对于一个二入一出的模糊控制器,一次推理过程中最多只激活4条控制规则,在控制的过程中只考虑这4条控制规则.这2种思想的结合使得控制规则的设计大大简化,可以采用批处理的方式设计控制器,不仅加快了系统的动态响应,也提高了控制精度.仿真结果证实了这种控制方法的有效性.     

遗传算法优化的模糊+变论域自适应模糊PID复合控制策略

针对变风量空调系统的不确定性和参数整定困难问题,提出了一种用遗传算法优化的模糊+变论域模糊PID复合控制器的新方法.该控制器由模糊控制和变论域模糊PID控制两部分组成.在系统动态阶段,采用模糊控制使其具有最优的动态性能;当系统进入稳态阶段,采用变论域自适心模糊PID控制使其具有最优的稳态性能.用遗传算法对主控制器的PID参数值进行离线优化,将其作为在线调节的初值,通过变论域的模糊推理在线调整系统的PID参数,使之具有良好的自适应能力.用模糊平滑切换保证了两种不同控制方式的平稳过渡.将提出的复合控制策略应用于变风量窄调系统的室温串级控制中,计算机仿真结果表明,该方法使系统具有良好的动、稳态性能,抗干扰和鲁棒性好.     

Multiple Fuzzy Relation and Its Application to Coupled Fuzzy Control

Using semi‐tensor product (STP) of matrix, this paper investigates the fuzzy relation of multiple fuzzy and uses this to design coupled fuzzy control is designed. First of all, under the assumption that the universe of discourse is finite, a fuzzy logical variable can be expressed as a vector, which unifies the expression of elements, subsets, and fuzzy subsets of a universe of discourse. Then, the matrix expression of set mappings is naturally extended to fuzzy sets. Second, based on STP, logic‐based matrix addition and product are proposed. These are particulary useful for the calculation of compounded fuzzy relations. Third, a dual fuzzy structure is introduced, which assures the finiteness of the universe of discourse, and is used for fuzzification and defuzzification. Finally, using the results obtained, a new technique is developed to design a coupled fuzzy controller for multi‐input multi‐output (MIMO) systems with coupled multiple fuzzy relations.     

Analytical structures and analysis of the simplest fuzzy PDcontrollers

This paper deals with simplest fuzzy PD controllers which employ only two fuzzy sets on the universe of discourse of each input variable, and three fuzzy sets on the universe of discourse of output variable. First, analytical structures of the simplest fuzzy PD controllers are derived via triangular membership functions for fuzzification, intersection T-norm, Lukasiewicz OR and Zadeh (1965) OR T-conorms, Mamdani's minimum, Larsen's product and drastic product inference methods, and center of area method for defuzzification. Properties of such fuzzy PD controllers are investigated. Based on these properties a comparative study is made on fuzzy controllers derived, and also on the fuzzy controllers and their counterpart-conventional linear PD controller. Finally, sufficient conditions for bounded-input bounded-output stability of fuzzy PD control systems are established using the well known small gain theorem.     

Analytical structures and analysis of the simplest fuzzy PI controllers

This paper deals with simplest fuzzy PI controllers which employ two fuzzy numbers on the universe of discourse (UOD) of each input variable, and three fuzzy numbers on the UOD of output variable. Analytical structures of such controllers are derived using triangular membership functions for fuzzification, different combinations of T-norms and T-conorms, different inference methods, and center of area (COA) method for defuzzification. Properties of these controllers are investigated. A comparative study is made on (i) the fuzzy PI controllers derived, and (ii) on the fuzzy PI controllers and their counterpart—conventional PI controller. Moreover, sufficient conditions for bounded-input bounded-output (BIBO) stability of fuzzy PI control systems are established using the well-known small gain theorem.     

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     

基于神经-模糊控制系统的移动机器人动态路径规划

针对机器人在未知、复杂环境下从源到目标之间,避开各种类型的障碍的问题,设计了系统的神经-模糊控制算法进行动态路径规划：设计了合理的模糊推理体系,实现输入模糊化、模糊推理规则库、输出去模糊化控制;根据规则库设计神经网络结构,简化网络结构和参数;采用QPSO算法训练网络;状态变量的存储和管理策略,解决了“U”型障碍物内的死循环路径问题。实验结果表明,在以上算法的控制下,机器人能够朝着目标,规划产生合理的路径,不会陷入死循环。     

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.     

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.     

Sensitivity analysis in fuzzy systems: Integration of SimLab and DANA

This work addresses the sensitivity analysis on the settings of fuzzy inference systems where multiple, yet dissimilar inputs are aggregated and resolved into a single output. The JAVA platform-independent development environment DANA (Data Analysis aNd Assessment) includes components for computation of fuzzy-logic based rules guiding the fuzzification–aggregation–defuzzification process by alternative membership functions (of an input to belong to fuzzy sets) and weighing systems, and unlimited aggregation levels. The integration of the library for sensitivity analysis SimLab within DANA is illustrated using the Morris screening method to determine the relative importance ranking of fuzzy settings (lower and upper limits, weights) on the final defuzzified output. The assumptions about the settings of the analysis are also discussed using an illustrative example where sensitivity analysis is applied to the fuzzy inference system designed to evaluate analytical methods in the domain of genetically modified organisms.     

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。