基于改进蚁群算法的模糊控制器优化设计

Optimization Design of Fuzzy Controller Based on Improved Ant Colony Algorithm

研究控制器优化问题,由于模糊控制系统参数无法同时优化,使得系统选择参数困难,使系统控制效果存在一定的缺陷,安全性和可靠性降低。为解决上述问题,提出了一种多种群进化蚁群算法对模糊控制器优化设计。采用懒蚂蚁效应的改进蚁群算法进行优化,在传统蚁群算法的基础上,采用多个种群并行,对算法的初始化、路径构建以及信息素更新改进,并引入到模糊控制器的隶属函数、模糊规则的优化搜索中,搜索出适应于不同控制阶段的模糊控制器参数及控制规则,并进行仿真。仿真结果证明了改进算法对模糊控制器的参数具有良好的搜索速度和精度,使系统有很强的鲁棒性。

Aimed at the problem of that the fuzzy parameters of fuzzy controllers can not been optimized synchronously, in this paper, a method of multi-colony evolvement ant colony algorithm based on idle ant colony system was proposed. This algorithm adopted multi-colony parallel optimization based on tradition ACO algorithm, the ACO implementation including data initialization, solution construction and pheromone update was improved. At the same time, a coding method of ACO algorithm was designed to guarantee the completeness and semantics of membership function. It can improve the quality of the solution space and raise the searching speed. Simulation result shows that this algorithm is feasible and effective.