基于遗传算法的模糊控制器动态优化方法  

Dynamic optimizing method of fuzzy controller based on genetic algorithm

作　　者：冯晓露[1] 覃来丰[1] 岑可法[1] 

机构地区：[1]浙江大学机械与能源工程学院,浙江杭州310027

出　　处：《浙江大学学报（工学版）》2007年第3期461-465,共5页Journal of Zhejiang University：Engineering Science

摘　　要：为了克服电厂主蒸汽温度被控对象的大迟延性、模型不确定性和时变性，提出了一种新的基于遗传算法（GA）的动态模糊控制器优化方法．该方法根据被调量的当前偏差值和偏差变化值的大小，采用改进的遗传算法和错时修正的方法，每次只对模糊控制规则表中的一个当前被激活的控制量修正值进行优化，对模糊控制器控制规则表中的数据进行实时在线的动态优化．仿真结果表明，当主蒸汽温度被控对象的模型参数变化10％时，采用该动态优化方法的模糊控制系统可以将主蒸汽温度动态偏差控制在-3～3℃，调节时间缩短了至少40％，主蒸汽温度控制系统的动态特性得到了有效的改善．To overcome the problems of long time-delayed, uncertainty and time-variant of main-steam temperature control object in power plant, a dynamic optimizing method for the fuzzy controller was proposed based on genetic algorithm（GA）. According to the current error and the error change of the modulated variable, the improved GA and the staggered amendment method were adopted, and only a current activated amendment value of the fuzzy control rule table was optimized at each time. The data in the control rule table of fuzzy controller were dynamically optimized real-timely and on-line. The simulation results show that when the model parameter of the main-steam temperature control object changes by 10%, the fuzzy control system optimized by the proposed controller dynamic optimizing method can limit dynamic difference of main-steam temperature within -3 - 3℃, and can shorten the regulating time by 40% at least. The fuzzy control system can efficiently improve the dynamic character of main-steam temperature control system.

关 键 词：主蒸汽温度控制 迟延性 模糊控制器优化 遗传算法 在线优化 

分 类 号：TK223.7[动力工程及工程热物理—动力机械及工程]