基于免疫遗传算法优化的模糊控制方法及其应用

在模糊控制器的设计过程中,为了使模糊控制器的性能达到全局优化,应用免疫遗传算法对模糊控制器参数进行优化设计;在综合考虑各种参数对控制器性能影响的基础上,给出了一种全面优化隶属度函数、比例因子和量化因子的优化方法;利用了免疫算法能保持个体的多样性和能对学习过程进行引导的特点,对模糊控制器的多个参数同时进行优化,从而显著提高了系统的收敛性、稳定性。应用该方法对数控铣削加工过程的模糊控制器的设计进行了仿真,并与其他方法进行比较和控制实例的验证,表明了该基于免疫遗传算法优化的模糊器能获得更优良的控制性能。     

基于一种混沌BP算法的神经模糊控制器的优化

在模糊控制器设计问题的研究中,结合神经网络和模糊控制的优点,设计了一种全网络化的模糊控制器,使模糊推理的实现过程网络化,清晰化.针对BP算法学习速度慢、易陷入局部最小的缺点,引入混沌思维,提出了基于混沌Logistic方程的BP混合学习算法(CBP),将用于神经模糊控制器参数的优化设计中,使设计的神经模糊控制器具有更优的性能.通过仿真对算法及控制器进行验证,仿真结果表明,上述算法能有效地优化神经模糊控制器的参数和结构,所设计的神经模糊控制器具有较好的性能.     

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。     

基于粒子群优化的一类模糊控制器设计

针对一般模糊控制器存在稳态性能与动态性能之间的矛盾,提出一种参数自整定模糊控制器.该控制器结构简单,算法简便,具有良好的动态特性,能有效消除静态偏差,且有一定的鲁棒性.为避免模糊控制器设计中参数调试的复杂性,获得最佳的控制性能,应用改进的自适应粒子群优化算法对模糊控制器参数进行优化设计.通过典型的被控对象的仿真研究,验证了所提出算法的有效性和适应性以及所设计控制器的优越性.     

基于PSO的模糊控制器参数优化设计

本文提出了将微粒群优化(PSO)算法应用于模糊控制器的参数优化设计中,针对常用的工业对象模型进行了仿真实验,仿真结果表明基于微粒群算法优化模糊控制器参数可以获得满意的控制效果,PSO算法为模糊控制器的设计提供了一种的新的思路.     

基于类前馈解耦的双模糊控制在电站锅炉燃烧系统的仿真研究

为了进一步提高目前电站锅炉燃烧系统的控制性能,在分析了某亚临界锅炉燃烧系统动态特性与影响因素的基础上,将各回路主控制器采用可以适时调整控制器参数的模糊自适应PID控制器,并且根据前馈补偿解耦原理在各耦合回路间增加类前馈补偿的模糊解耦控制器来实现全新的燃烧系统优化设计;通过MATLAB仿真表明,采用文章提出的双模糊控制策略相比常规的模糊自适应PID控制,响应时间缩短了45.2%,超调量降低了53.6%,调节时间减少了32.7%,鲁棒性增强,控制性能有了很大提升。     

一类Takagi-Sugeno模糊控制器的自适应遗传优化设计

提出了一类Takagi-Sugeno模糊控制器的自适应遗传优化设计方法。采用实数编码方式,并由自适应交叉和变异概率来控制遗传操作,有效地提高了参数优化的精度和算法的寻优效率。在优化过程中引入对称性参数约束条件,大大减小了算法的搜索空间。将该算法用于倒立摆T-S模糊控制器的设计,实现了控制器参数的快速自动整定。仿真结果表明,获得的T-S模糊控制器具有优良的性能。     

基于遗传算法的模糊控制器的综合优化设计

该文简要叙述了模糊控制器的优化设计原理,提出了一种采用遗传算法对模糊控制器的量化因子、比例因子、隶属函数的参数和模糊控制规则进行综合优化的设计方法,并采用VisualC++6.0编制了相应的软件,该软件已成功地应用于工程中。此外,该软件也可作为遗传优化和模糊控制器设计的辅助教学软件。     

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.     

基于多步预测性能指标的模糊控制器参数优化设计

针对一类PID型模糊控制器，提出基于多步预测性能指标的模糊控制器参数优化设计方法．通过最小化多步预测性能指标和调整比例因子，定量给出了控制器的设计准则，对于现场操作人员具有一定的指导意义．仿真实例验证了算法的有效性和鲁棒性．     

基于LabVIEW的模糊控制器设计与仿真

以LabVIEW编程软件为基础,介绍了其中控制模块中模糊逻辑工具包(Fuzzy LogicToolkit)中的子程序(VI),并应用其中模糊逻辑控制器设计VI构建模糊控制器的方法。设置隶属度函数,建立模糊控制规则,创建模糊推理关系,实现对模糊控制器设计的具体步骤,并结合电液伺服模糊控制系统实例,利用模糊逻辑(Fuzzy logic)模块及LabVIEW提供的仿真模块(Simulation Module)建立系统仿真框图。通过仿真曲线,分析模糊控制器控制效果及其影响因素,从而大大缩短模糊控制器的设计周期,具有较大的工程实用价值。     

Simplifying the automatic design of a fuzzy logic controller using evolutionary programming

With the availability of a wide range of Evolutionary Algorithms such as Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the MacVicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.     

模糊逻辑控制系统分析与设计

本文把模糊逻辑控制当作多维继器，用Ｌｐ稳定性和园周判据分析闭环模糊逻辑控制系统的稳定性，给出闭环非线性系统与模糊逻辑控制器结合系统的稳定判据和设计方法，并用计算机仿真试验，进一步将稳定判据应用到不同系统。     

Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms

In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.     

Single-chip fuzzy logic controller design and an application on a permanent magnet dc motor

This paper describes a low-cost single-chip PI-type fuzzy logic controller design and an application on a permanent magnet dc motor drive. The presented controller application calculates the duty cycle of the PWM chopper drive and can be used to dc–dc converters as well. The self-tuning capability makes the controller robust and all the tasks are carried out by a single chip reducing the cost of the system and so program code optimization is achieved. A simple, but effective algorithm is developed to calculate numerical values instead of linguistic rules. In this way, external memory usage is eliminated. The contribution of this paper is to present the feasibility of a high-performance non-linear fuzzy logic controller which can be implemented by using a general purpose microcontroller without modified fuzzy methods. The developed fuzzy logic controller was simulated in MATLAB/SIMULINK. The theoretical and experimental results indicate that the implemented fuzzy logic controller has a high performance for real-time control over a wide range of operating conditions.     

Design of a hybrid fuzzy logic proportional plus conventionalintegral-derivative controller

Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output “small gain theorem”, the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance     

Fuzzy logic based smart traffic light simulator design and hardware implementation

The objective of this study is to develop fuzzy logic based traffic junction light simulator system for design and smart traffic junction light controller purposes and also to observe its performance. Traffic junction simulator hardware is developed to overcome difficulties of working in a real environment and to easily test the performance of the controller. By using the traffic light simulator developed in this study, results of constant duration (conventional) traffic light controller and fuzzy logic based traffic light controller are compared where the vehicle inputs are supplied by the simulator. Statistical experimental results obtained from the implemented simulator show that the fuzzy logic traffic light controller dramatically reduced the waiting time at red lights since the controller adapts itself according to traffic density. It is obvious that the intelligent light controller is going to provide important advantages in terms of economics and environment.     

基于遗传算法和模糊逻辑的智能飞控技术研究

设计了基于遗传算法和模糊逻辑控制的智能飞行控制系统及采用论域自调整的模糊控制器，控制器以角度跟踪误差及其微分信号为输入来控制相应的气动舵面偏转，实现对该姿态的跟踪控制。文中给出了控制器输入输出的隶属函数，设计了相应的规则库。并在此基础上进一步利用遗传算法对模糊控制器进行优化设计，给出了遗传算法各个参数的选择原则。仿真结果表明，基于遗传算法和模糊逻辑的智能飞控系统具有良好的控制效果。     

应用MATLAB设计炼钢电弧炉电极模糊控制器

用模糊控制方法对炼钢电弧炉电极进行控制,叙述电极模糊控制器的设计思想,并用Matlab及模糊逻辑工具箱实现该二维模糊控制器。采用Simulink工具箱动态仿真,计算机仿真表明控制效果比较满意。     

Soft computing technique for power control of Triga Mark-II reactor

In this study, a trajectory tracking fuzzy genetic controller for Istanbul Technical University Triga Mark-II nuclear research reactor design approach is given. Power output of reactor is controlled along the predefined trajectory by fuzzy logic controller. Designed zero order Sugeno type fuzzy logic controller membership boundary value and rule weights are found by genetic algorithm. Non-chattering control with smooth control surface is also achieved using constrained fitness functions. Simulation results shows that reactor power successfully tracks the given trajectory under various working conditions and reaches the desired power level within the determined period within small tracking error.