基于金豺优化算法的PID参数优化研究

Research on PID parameters optimization based on golden jackal optimization algorithm

针对控制系统PID参数难以寻优的问题,提出一种金豺优化(Golden Jackal Optimization,GJO)算法。该算法受金豺家族狩猎过程的启发,以PID的3个参数组成金豺的位置坐标,根据金豺的狩猎规则更新公豺和母豺豺对的位置,使其向最优解迭代。为了验证算法的效果,将该算法分别与Z-N临界比例法、粒子群优化算法和灰狼优化算法进行比较。仿真结果表明,采用金豺优化算法控制系统的PID参数,系统具有调节时间短、上升速度快和超调量小等优点,为PID参数优化提供了参考。

Aiming at the problem that better PID parameters of control system are difficult to find,a new kind of swarm intelligence optimization algorithm called Golden Jackal Optimization(GJO)algorithm was proposed.The algorithm is inspired by the hunting process of the golden jackal family,it uses three parameters of PID as the position of golden jackal and updates position of male and female jackal pairs according to the hunting rules.Finally,the soultion iterates toward the optimal solution.To verify the effects,the algorithm was compared with the Z-N formula method,the particle swarm optimization algorithm,and the gray wolf optimization algorithm.The simulation results show the system has the advantages of shorter adjustment time,faster rise time and smaller overshoot.It provides a reference for PID parameter optimization.