基于遗传算法的模糊控制器参数优化

Parameters optimization of fuzzy controller based on genetic algorithm

针对非线性量化因子模糊控制器的参数对系统性能影响和参数间的相互制约,提出了一种基于遗传算法的参数整定与优化方法,并进行了仿真研究。仿真结果表明通过该方法寻优的系统具有更好的响应速度和控制精度;当对象结构或参数发生变化时,非线性量化因子模糊控制器可以重新整定参数,以保持良好的控制效果,具有很强的鲁棒性。

In view of effects of the parameters of fuzzy controller with nonlinear scaling factors on a system＇s performance and interaction between parameters, this paper proposes a method based on genetic algorithm （GA） to tune and optimize the parameters. Simulation results show that systems which adopt the parameters derived from the method are better in dynamics and static property. When the parameters or the structure of plant is changed, a fuzzy controller with nonlinear scaling factors can maintain good performance indicators through re-tuning the parameters and has stronger robustness.