基于自适应蚁群遗传混合算法的PID参数优化

Optimization design based on self-adapted ant colony algorithm and genetic algorithm for parameters of PID controller

针对遗传算法易重复迭代、蚁群算法易陷入停滞的缺点,提出基于自适应蚁群遗传混合算法的PID参数优化。先用遗传算法获得PID参数的初值,再用改进后的蚁群算法自适应调整路径选择概率和信息素更新规则,最终搜索出PID参数的最优值。仿真结果表明,对于给定的被控对象,相比于GA和ACS算法,该算法搜索出的Kkp、Kki、Kkd最优,系统响应时间短,动态性和稳定性佳,说明该方法整定出的PID参数值具有最优性。对于其他的控制对象和过程也具有参考价值。

This paper proposed a method of self-adapted ant colony algorithm and genetic algorithm for the optimization of parameters of PID controller. This method overcame genetic algorithm＇s defects of repeated iteration,ant colony algorithm＇s defects of got stagnation. This algorithm got initialized pheromone applying genetic algorithm to get PID parameters. Then ran an improved ant colony algorithm,adjusted the influence of each ant to the trail information updating and selected probabilities of the paths. Eventually,obtained the optimal value of PID parameters. For a given system,the results of simulation experiments which compare with Z-N,GA and ACS,the response time is greatly reduced. at the same time the system has good performance and stability. It illustrate that the method is more optimality for setting the value of PID. The experiments show that it also can be used for other process widely.