基于基因算法的模糊控制研究

Research on Genetic Algorithms-Based Fuzzy Control

本文提出一种基于基因算法优化的自学习模糊控制器的设计 .研究基因算法理论对模糊控制参数的全局寻优 .出于对硬件实现的考虑 ,集中讨论了控制规则后件的产生及对规则库的动态学习 ,并采用对传统基因算法进行变化后的计算方法 .使用Matlab中的仿真工具Simulink ,对倒立摆典型非线性系统进行了在线模拟 ,证实了所提出的控制算法的有效性和适用性 。

This paper proposes a design of the self adaptive learning fuzzy controller based on Genetic Algorithms optimization.Researching how this theory optimizes the parameters of fuzzy control in the whole scope is taken.Considering the implementation of hardware,the discussion is focused on the generation of control rules and the dynamic learning of the rules store.Some new computational methods are used based on traditional genetic algorithms.The nonlinear control system of inverted pendulum is simulated on line with this design,by simulate tools Simulink in Matlab,which proves the validity and the applicability of this proposed control method.This approach also provides a valuable theory basis to the implementation of hardware chip.