基于改进人工蜂群算法的PID参数整定研究

PID parameter tuning based on improved artificial bee colony algorithm

针对目前PID参数整定方法收敛精度低和收敛速度慢的问题,提出了在PID参数整定中引入向当前种群最优解学习的改进人工蜂群算法,不仅可以保持种群的多样性,防止算法陷入局部最优,而且可以缩短运算时间。仿真实验结果表明,改进人工蜂群算法的收敛精度和收敛速度均优于标准人工蜂群算法。

Aiming at the low convergence precision and the slow convergence speed problem in the PID parameter tuning,this paper introduces the information of the current population optimal solution into the search of the improved artificial bee colony algorithm. Not only can maintain the diversity of the population and prevent the algorithm falling into a local optimum, but also can save operation time. The simulation experiment results show that the convergence precision and the convergence speed of the improved artificial bee colony algorithm are better than standard artificial bee colony algorithm.