感应电机DTC系统中遗传模糊自适应PI控制研究

针对感应电机(IM)直接转矩控制(DTC)中采用常规模糊控制器带来超调量大,响应慢,脉动大等问题,提出一种基于遗传算法和模糊自适应PI控制的速度调节器设计方案。它采用遗传算法优化模糊自适应PI控制器的模糊控制规则和量化因子Ke,Kec及比例因子KuP,KuI,以确保IM的DTC系统响应具有最优的动态和稳态性能。仿真与试验结果分析表明,与传统模糊PI控制相比,该设计方法具有适应性强,动态响应快,鲁棒性好等优点,取得了较满意的控制效果。     

基于模糊神经网络的VSC-HVDC系统控制器的研究

对同步旋转坐标系下的VSC-HVDC系统的控制策略进行研究,针对传统的PI控制在大扰动下难以精确控制的缺陷,设计了一种新的控制方案及控制器。采用双环控制,其中内环电流控制器采用PI控制,而外环控制器采用模糊控制与人工神经网络相结合的控制策略,并采用遗传算法优化模糊神经网络的连接权值和隶属度函数。整流侧有功功率跌落、三相短路等情况下的仿真结果表明,此种控制方法在大扰动下的动态稳定性明显优于传统PI控制。     

STATCOM模糊控制的综合优化设计

针对严重非线性静止同步补偿器模糊逻辑控制隶属函数的参数和模糊控制规则调整困难的问题,根据微遗传算法,并利用模糊系统的逼近能力,提出了一种模糊控制综合优化设计方法.选择模糊控制器的输入/输出观测信号,应用MATLAB中的模糊控制工具箱快速地获得初始的模糊控制规则,再采用微遗传算法优化其隶属函数参数和模糊控制规则,并给出了模糊控制规则优化后的结果.对带有一个静止同步补偿器的四机电力系统,在三种不同的负荷和三种不同的控制方式下进行仿真实验,结果表明:在宽负荷变化范围内,该控制器的追踪控制能力和鲁棒性等比常规的超前滞后控制、未优化模糊控制都优越.     

基于模糊自适应PI控制的开关磁阻电机直接转矩控制

针对常规模糊控制器在开关磁阻电机直接转矩控制（DTC）中的超调量大、响应慢、脉动大等问题,提出一种基于遗传算法的模糊自适应PI控制器的速度调节器设计方案,结合遗传算法与模糊PI控制,应用遗传算法优化模糊自适应PI控制器的模糊控制规则和量化因子及比例因子,以确保开关磁阻电机直接转矩控制系统响应具有最优的动态响应和稳态性能。仿真与试验结果分析表明,与传统模糊PI控制相比,该设计方法具有适应性强、动态响应好、鲁棒性强等优点,取得了比较满意的控制效果。     

无刷直流电动机模糊PI直接转矩控制研究

针对常规模糊控制器在无刷直流电动机直接转矩控制(DTC)中的超调量大、响应慢和脉动大等问题,提出一种基于遗传算法的模糊自适应PI控制器的速度调节器设计方案,结合遗传算法与模糊PI控制,应用遗传算法优化模糊自适应PI控制器的模糊控制规则和量化因子Ke、Kec及比例因子KuP、KuI,以确保无刷直流电动机直接转矩控制系统响应具有最优的动态响应和稳态性能。仿真与试验结果表明,与传统模糊PI控制相比,该设计方法具有适应性强、动态响应好和鲁棒性强等优点,取得了比较满意的控制效果。     

遗传算法在模糊控制器参数寻优中的应用研究

介绍遗传算法的基本原理及实现步骤，并运用模糊控制器的参数寻优中，对复杂被控对象实现了最优控制，模糊控制规则采用基于解析表达式的模糊数模型，用遗传算法对模糊控制器中的量化因子和比例因子及偏差加权因子进行编码，确定适应度函数，在复制，交叉，变异的进化过程中获得全局最优点，改善模糊控制性能。在Matlab环境中实现遗传算法各算子的编程，并以二阶系统为例，对常规模糊控制器与经过遗传处寻优后的模糊控制器进行了仿真比较，其结果表明采用遗传算法寻优能获得较好的控制效果，该方法具有良好的动静态特性。     

基于PLC带自修正因子模糊控制自动配料系统研究

针对精细化工、建筑等行业的自动精确配料的问题,提出了一种基于PLC控制,算法采用带自修正因子模糊控制方法,设计了一种在误差、误差变化率、控制量语言变量的全论域范围内带有自修正因子模糊控制器.研究结果表明,采用带自修正因子模糊控制系统,比传统PID调节器、传统的模糊控制器有系统输出响应快、无超调、对参数有较强的鲁棒性等优点,能够很好地解决自动精确配料的问题,从而产生了较高的经济效益.     

基于遗传算法的模糊控制器规则优化

模糊控制器设计的关键问题就是模糊控制规则的选择。基于遗传算法的模糊控制规则表优化,是为模糊控制提供一种更加方便、有效的查表法。为了提高模糊控制器的性能,提出了基于遗传算法的模糊控制器规则表的优化方法,并进行了仿真实验。仿真结果表明,在模糊控制中采用遗传算法使控制系统最终达到所要求的控制效果,证明了该方法的可行性和有效性。     

遗传优化模糊免疫PID在梭式窑温度控制中的研究

将遗传算法、模糊控制和免疫反馈机理与传统的PID控制相结合,提出了一种基于遗传整定的模糊免疫PID控制器并将它应用在梭式窑温度控制中,其中免疫比例控制器的非线性函数由模糊推理实现,PID及免疫比例控制器的参数由遗传算法实现寻优.实验结果表明,该控制方法的控制性能优于常规的PID控制,具有一定的推广应用价值.     

电源电流控制的并联有源电力滤波器研究

介绍了并联有源电力滤波器(APF)的一种新的控制方式,即电源电流控制方式,该控制方式与传统控制方式是完全等效的.采用带有自调整因子的模糊控制规则设计了逆变器直流侧电压的模糊控制器,并将模糊控制与PI控制相结合,设计了复合控制器,使其兼具模糊控制鲁棒性强和Pl控制稳态精度高等优点.实验结果表明,新的控制系统完全能够补偿谐波和无功电流,并且具有电压超调量小、电流冲击小等优点.     

基于遗传算法的窄间距隶属函数在模糊逻辑电力系统稳定器设计中的应用

提出了一种简单、高效的模糊逻辑电力系统稳定器的设计方案,其特点是采用了窄间距隶属函数(SSMFs).SSMFs是设计模糊逻辑控制器隶属函数的一种新方法.采用遗传算法选择SSMFs模糊逻辑控制器的最佳参数值,包括间距系数、重叠系数和增益系数等.通过单机无穷大系统验证所提方案的正确性.结果表明,所提出的SSMFs模糊逻辑电力系统稳定器在发电机负载和励磁增益大范围变化情况下,改善了电力系统的稳定性.     

Design of PSO‐based fuzzy logic controller for single axis magnetic levitation system

In this paper, a particle swarm optimization (PSO) method is proposed to design an optimal robust fuzzy logic controller (FLC). The objective of this paper is to design a nonlinear optimal robust controller for the single axis magnetic levitation system with high accuracy. PSO algorithm is applied to search globally optimal parameters of FLCs. Three different FLCs are designed. First, proportional derivative (PD)‐like FLC. Second, the FLC is based on the PSO algorithm to find the optimal range of the eight linguistic membership functions (FLC1 with PSO algorithm). Finally, the FLC is based on the PSO algorithm to find the optimal range and shape of the four linguistic membership functions (FLC2 with PSO algorithm). The performances of three different FLCs are compared. Simulation results show that PSO‐based optimal FLCs find the optimal range and shape of the four linguistic membership functions and achieved better performance than the other proposed controllers, minimizing 48 fuzzy rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Damping of low‐frequency oscillations using an SVC‐based supplementary controller

In this paper, the optimal tunning problem of parameters of a conventional lead–lag controller (LLC) and a fuzzy logic controller (FLC) based on the static Var compensator (SVC) is considered. The solution is obtained using an improved particle swarm optimization (IPSO) algorithm. The membership functions and scaling factors of the FLC and LLC parameters are optimized using the above‐mentioned technique. The proposed controllers are settled down to an SVC that is installed at the middle of a transmission line connecting a single machine to an infinite bus system. Simulation results show the superiority of the optimized FLC compared to the optimized LLC and also when the SVC is without the supplementary controller under different disturbances and loading conditions. The simulations and analyses are implemented in MATLAB environment. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Optimal design for fuzzy controllers by genetic algorithms

Fuzzy control has been applied to various industrial processes; however, its control rules and membership functions are usually obtained by trial and error. Proposed in this paper is an optimal design for membership functions and control rules simultaneously by a genetic algorithm (GA). GAs are search algorithms based on the mechanics of natural selection and natural genetics. They are easy to implement and efficient for multivariable optimization problems, such as fuzzy controller design. The simulation result shows that the fuzzy controller thus designed can achieve good performance merely by using a few fuzzy variables     

Ant Colony Optimized Fuzzy Control Solution for Frequency Oscillation Suppression

This paper presents a novel approach in addressing a critical power system issue, i.e., automatic generation control (AGC) in a smart grid scenario. It proposes the design and implementation of an optimized fuzzy logic controller (FLC) for AGC of interconnected power network. There are three different sources of power generation considered in the two-area interconnected model of power system network. First area is equipped with a single reheat thermal unit and a superconducting magnetic energy storage (SMES) unit, while another area has a hydro-unit with SMES. A multi-stage optimization strategy for the optimal solution of FLC for tie-line and frequency oscillation suppression is proposed in this paper using an ant colony optimization technique. The optimization of FLC is carried out in four different stages. The first stage is the optimization of range of input and output variables; the second stage is the optimization of membership function; the third and fourth stages are the optimization for rule base and rule weight optimization, respectively. The performance of the proposed controller is also compared with another control approaches to stabilize P_tie-line and Δf oscillations; these are the Ziegler–Nichols-tuned proportional–integral–derivative (PID) controller and genetic algorithm optimized PID controller. A comprehensive analysis of the traditional techniques and proposed techniques is presented on the basis of major dynamic performance parameters, i.e., settling time and peak overshoot.     

多目标满意优化在模糊控制器设计中的应用

提出了一种应用多目标满意优化模型进行模糊控制器优化设计的方法。给出了计算模型和算法。该方法以满意度函数反映设计者对性能指标的评价，更符合实际要求。应用该方法对模糊控制器的隶属函数参数以及控制规则库进行优化设计。仿真结果表明了该方法的有效性和实用性。     

水轮发电机组GA模糊控制器研究

模糊控制器设计的困难之一就是确定量化因子和隶属度函数，它们对控制器性能具有重要影响。文中提出用遗传算法（GA）对水轮发电机组模糊控制器进行优化设计，描述了控制器结构，优化算法和动态仿真结果。仿真对比试验表明，经过GA优化的模糊控制器比经过MATLAB NCD工具箱优化的PID控制器具有更好的控制效果和更强的鲁棒性。     

遗传模糊算法在短期负荷预测中的应用

提出了一种基于模糊逻辑原理的负荷预测方法,使用遗传算法对系统参数进行训练。在以往的模糊逻辑系统建立过程中,其主要参数(如模糊推理规则和隶属函数等)需要依靠运行人员经验或专家知识来确定,而本文利用遗传算法,通过对样本数据的自学习过程来获取系统参数。在遗传算法中,将推理规则与隶属函数参数的确定结合在一起,从而确定系统参数的最优组合,由此建立起一个较合理的模糊负荷预测系统。仿真实验结果表明,该方法能够达到满意的预测精度,具有良好的实用前景。     

Tracking control of induction motor using fuzzy phase plane controller with improved genetic algorithm

This paper proposes a fuzzy phase plane controller (FPPC) using an improved genetic algorithm (IGA) for the optimal position/speed tracking control of an induction motor. The proposed optimal algorithm (IGA) is equipped with an improved evolutionary direction operator (IEDO) to enhance the traditional genetic algorithm (GA). An application example was considered to compare the proposed IGA with the GA. Computational results show that the proposed IGA is more efficient than the GA.Fuzzy membership functions, phase plane theory and the proposed IGA are employed to design the proposed controller (FPPC) for the optimal position/speed tracking control of an induction motor. The proposed FPPC has the merits of rapid response, simple designed fuzzy logic control and an explicitly designed phase plane theory. Simulated and experimental results reveal that the proposed FPPC is superior in the optimal position/speed tracking control to conventional PI controllers.