基于灰狼优化算法的PID控制器设计  

A PID Controller Design Based on the Grey Wolf Optimization Algorithm

作　　者：冯严冰 FENG Yanbing(Jiangsu Urban Vocational College,Changzhou 232001,China)

机构地区：[1]江苏城市职业学院,江苏常州232001

出　　处：《传感器世界》2022年第8期17-21,共5页Sensor World

摘　　要：为了寻找最佳的比例、积分和微分(PID)控制器增益因子,使系统输出尽可能接近参考输入信号的变化,使得控制系统的时间加权绝对误差积分最小,提出了基于灰狼优化算法(GWO)进行PID控制器增益因子优化设计的方法。为了评估该方法的性能,针对设定点跟踪设计目标,通过使用3种基准线性数学模型进行仿真测试,并与粒子群优化算法(PSO)、引力搜索算法(GSA)得到的仿真结果进行了比较。仿真结果表明,基于灰狼优化算法的最优参数PID控制器比基于其他算法的性能更好,最优值更佳。To find the optimal proportional,integral,and differential(PID)controller gain factor,make the system output as close as possible to the reference input signal,minimize the time-weighted absolute error integral of the control system,in the paper,PID controller gain factor optimization design based on grey wolf optimization algorithm.To evaluate the performance of the proposed method,track design goals for setpoints,by performing simulation testing using three benchmark linear mathematical models,compared with the simulation results obtained by the particle swarm optimization algorithm and the gravitational search algorithm.The simulation results show that the optimal parameter PID controller based on the grey wolf optimization algorithm performs better and is better than that based on other algorithms.

关 键 词：灰狼优化算法 引力搜索算法 粒子群优化算法 PID控制器 

分 类 号：TH161[机械工程—机械制造及自动化]