학술논문
基于改进粒子群算法的PID控制参数优化 / Optimization of PID Control Parameters Based on Improved Particle Swarm Algorithm
Document Type
Academic Journal
Author
Source
组合机床与自动化加工技术 / Modular Machine Tool & Automatic Manufacturing Technique. (2):89-98
Subject
Language
Chinese
ISSN
1001-2265
Abstract
针对传统粒子群算法存在收敛速度慢,收敛精度低以及易陷入局部最优的问题,提出了一种融合中垂线策略的中垂线粒子群算法(MAPSO),同时引入惯性权重余弦调整策略,避免算法陷入局部最优.基于中垂线策略的游离粒子位置更新方法,能够加快粒子的收敛速度,从而增强算法的寻优速度和寻优精度.将改进的粒子群算法用于PID控制器参数优化,与Ziegler-Nichols(Z-N)公式法、线性递减惯性权重粒子群优化算法(MeanPSO)进行对比实验,结果表明中垂线粒子群算法精度更高,能够快速地整定PID参数,使控制系统响应函数性能指标更好.
To address the problems of slow convergence speed,low convergence accuracy and easy to fall into local optimum of traditional particle swarm algorithm,proposes a mid-pipeline particle swarm algo-rithm(MAPSO)incorporating mid-pipeline strategy,and introduces inertia weight cosine adjustment strate-gy to avoid the algorithm falling into local optimum.The method of updating the position of free particles based on the mid-drop line strategy can speed up the convergence of particles,thus enhancing the speed and accuracy of the algorithm for finding the best.The improved particle swarm algorithm is used for PID con-troller parameter optimization and compared with Ziegler-Nichols(Z-N)formula method and linear de-creasing inertia weight particle swarm optimization algorithm(MeanPSO)for experiments.
To address the problems of slow convergence speed,low convergence accuracy and easy to fall into local optimum of traditional particle swarm algorithm,proposes a mid-pipeline particle swarm algo-rithm(MAPSO)incorporating mid-pipeline strategy,and introduces inertia weight cosine adjustment strate-gy to avoid the algorithm falling into local optimum.The method of updating the position of free particles based on the mid-drop line strategy can speed up the convergence of particles,thus enhancing the speed and accuracy of the algorithm for finding the best.The improved particle swarm algorithm is used for PID con-troller parameter optimization and compared with Ziegler-Nichols(Z-N)formula method and linear de-creasing inertia weight particle swarm optimization algorithm(MeanPSO)for experiments.