两种群智能算法在PID参数优化中的应用分析  

Application Analysis of Two Population Intelligent Algorithm in PID Parameter Optimization

作　　者：莫程凯 赵宇红[1] 段灵芝 王浩伦 MO Cheng-kai;ZHAO Yu-hong;DUAN Ling-zhi;WANG Hao-lun(School of Electrical Engineering,University of South China,Hengyang 421001,China;Hunan Normal University,Changsha 410081,China)

机构地区：[1]南华大学电气工程学院,湖南衡阳421001 [2]湖南师范大学,湖南长沙410081

出　　处：《机械工程与自动化》2022年第1期181-182,186,共3页Mechanical Engineering & Automation

摘　　要：PID的参数优化是应用工程中非常关键的一个问题。针对PID的参数优化,分析了两种最常用、经典的群智能算法——粒子群算法和蚁群算法在PID参数优化中的应用。两种群智能算法与PID控制器的结合适用于不同场合,各有优势和不足。两种群智能算法都能够有效地对PID参数进行整定,使得系统得到更好的鲁棒性和精确度。PID parameter optimization is a very key problem in application engineering. Aiming at PID parameter optimization, this paper analyzes the application of two most commonly used and classical swarm intelligence algorithms: particle swarm optimization algorithm and ant colony algorithm in PID parameter optimization. The combination of two population intelligent algorithm and PID controller is suitable for different occasions, and each has its advantages and disadvantages. The two population intelligent algorithms can effectively adjust the PID parameters in real time, so that the system has better robustness and accuracy.

关 键 词：PID参数优化 粒子群算法 蚁群算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]