基于改进海马优化算法的PID参数优化  

PID Parameter Optimization Based on Improved Sea Horse Optimization Algorithm

作　　者：舒奕彬 李立君[1] 张振翮 戚浩 刘姜毅 SHU Yibin;LI Lijun;ZHANG Zhenhe;QI Hao;LIU Jiangyi(College of Mechanical and Electrical Engineering,Central South University of Forestry and Technology,Changsha Hunan 410004,China)

机构地区：[1]中南林业科技大学机电工程学院,湖南长沙410004

出　　处：《机床与液压》2024年第13期189-194,共6页Machine Tool & Hydraulics

基　　金：国家重点研发计划(2022YFD2202103)。

摘　　要：为了解决传统海马算法(SHO)在PID参数整定中存在全局寻优能力差且收敛速度慢的问题,提高PID参数优化质量,提出一种改进的海马优化算法(ISHO)。通过Tent混沌映射增加海马种群初始化多样性提高收敛速度;引入逃逸能量调控策略改进算法全局搜索与局部开发的转换机制,从而提高算法的全局寻优能力。将改进海马优化算法与传统海马算法、Z-N临界比例法、灰狼优化算法和粒子群优化算法进行比较,仿真结果表明:改进的海马优化算法优化PID参数具有调整时间更短、系统控制精度更高和收敛速度更快等优点,为PID控制系统的参数优化提供了参考。In order to solve the problem of poor global optimization ability and slow convergence speed of sea horse optimization(SHO)in PID parameter tuning,and improve the optimization quality of PID parameters,an improved sea horse optimization(ISHO)was proposed.Tent chaotic mapping was used to increase the initialization diversity of sea-horse populations and improve the convergence speed.The escape energy regulation strategy was introduced to improve the conversion mechanism between global search and local development of the algorithm,so as to improve the global optimization ability of the algorithm.The improved sea horse optimization was compared with the traditional sea horse optimization,Ziegler-Nlichols,gray wolf optimization and particle swarm optimization.The simulation results show that the improved sea horse optimization optimizes PID parameters with the advantages of shorter adjustment time,higher system control accuracy and faster convergence speed,which provides a reference for parameter optimization of PID control system.

关 键 词：比例积分微分控制器 海马优化算法 Tent混沌映射 逃逸能量调控策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]