液压与气动2025，Vol.49Issue(6)：9-17,9.DOI:10.11832/j.issn.1000-4858.2025.06.002
新型改进粒子群优化算法赋能气动滑台线性自抗扰运动控制
Novel Improved Particle Swarm Optimization Algorithm Empowering Linear Active Disturbance Rejection Motion Control of Pneumatic Sliding Table
摘要
Abstract
It is crucial to select appropriate control parameters to improve the motion control accuracy of the pneumatic sliding table.However,the traditional trial-and-error method is inefficient and heavily reliant on experience of parameter adjusters.Therefor,a novel improved particle swarm optimization algorithm is presented.This algorithm applies an improved Gaussian-sine chaotic mapping technique to generate initial particles so as to enrich the diversity of the population.It introduces sine disturbance and Lévy flight strategy to help particles escape local optima.Additionally,it integrates the sine-cosine algorithm and an improved slime mold algorithm to improve search accuracy.The experimental results from linear active disturbance rejection motion control tests on the pneumatic sliding table show that the proposed novel particle swarm optimization algorithm can effectively improve control accuracy.Compared with the trial and error method,it reduces the maximum steady-state error by 15.9％and 23.4％respectively when tracking sinusoidal trajectories with an amplitude of 150 mm and frequencies of 0.25 Hz and 0.5 Hz.And it reduces the maximum steady-state error by 13.5％when tracking multi frequency curves.关键词
气动滑台/高斯正弦混沌映射/线性自抗扰/运动轨迹跟踪Key words
pneumatic sliding table/Gaussian-sine chaotic mapping/linear active disturbance rejection/motion trajectory tracking分类
机械工程引用本文复制引用
冯志远,张智豪,钱墅,刘丽娇,浦晨玮,钱鹏飞..新型改进粒子群优化算法赋能气动滑台线性自抗扰运动控制[J].液压与气动,2025,49(6):9-17,9.基金项目
国家自然科学基金(52075223) （52075223）
大学生创新创业训练计划项目(202410299041Z,202310299910X) （202410299041Z,202310299910X）
