粒子群优化的模糊控制器设计

Design of a Fuzzy Controller Based on Particle Swarm Optimization

为避免模糊控制器设计中参数的复杂调试,并使其获得最佳控制性能,应用新颖的粒子群优化算法对模糊控制器参数进行优化设计。针对常规模糊控制器稳态精度欠佳的弱点,采用模糊控制与PID控制相结合的双模控制以有效消除静态偏差。通过对具有严重参数不确定性、多扰动以及大迟延的电厂主蒸汽温度被控对象的仿真研究,表明粒子群算法寻优速度快,计算量小,对模糊控制器参数的优化设计是非常有效的,使得主汽温控制系统在不同负荷下均获得很好的调节品质。

For avoiding complex adjustment of parameters as asked for in the design of fuzzy controllers, and attain optimal control properties, particle swarm optimization （PSO） algorithm has been made use of to optimize the parameters of a fuzzy controller during design. Noticing the shortcoming of lack of steady state precision of conventional controllers, dual model control by combined application of fuzzy and PLD control is used to effectively eliminate steady state deviations. Simulation study results on fresh temperature in power plants, which is characterized by parameter uncertainty, liable to disturbances and time-lay, show that PSO algorithm is distinguished by its ability of quick searching and of reducing calculation work required, thus providing a very efficient way of optimizing the parameters of fuzzy controllers, and herewith markedly introving control quality of the fresh steam temperature control system under all loading conditions. Figs 6, tables 2 and refs 8.