多目标粒子群优化算法在PID优化设计中的应用

Application of Multi-objective Particle Swarm Optimization Algorithm in PID Optimization Design

为了克服PID控制器参数整定时适应度函数权重难以选择的问题,提出一种多目标粒子群优化算法。该算法不需要设置权重参数,采用精英归档策略,按照Pareto最优原则,对控制系统阶跃响应的超调量和上升时间两项指标进行优化,从而实现PID控制器参数的自动整定。仿真结果表明,该算法可以获得大量优质Pareto最优解,解的质量优于其它方法。

In order to overcome the difficulty of choosing the weight of fitness function in PID controller parameter tuning,a multi-objective particle swarm optimization algorithm(MOPSO)was proposed.Using elite archiving strategy,MOPSO does not need to set weight parameters.According to the Pareto optimal principle,the control system of overshoot and rise time of the step response of the two indicators are optimized,thereby realizing the automatic tuning of PID controller parameters.Simulation results show that the algorithm can obtain a large number of high-quality Pareto optimal solutions,and the quality of the solutions is better than other methods.