基于PSO磁悬浮球系统自适应灰预测控制  

Adaptive Grey Predictive Control of Magnetic Levitation Ball System Based on PSO

作　　者：马晓东 魏利胜 MA Xiaodong;WEI Lisheng(School of Electrical Engineering,Anhui Polytechnic University,Anhui Wuhu 241000,China;Anhui Key Laboratory of Electric Drive and Control,Anhui Wuhu 241000,China)

机构地区：[1]安徽工程大学电气工程学院,安徽芜湖241000 [2]安徽省电气传动与控制重点实验室,安徽芜湖241000

出　　处：《重庆工商大学学报（自然科学版）》2023年第5期16-24,共9页Journal of Chongqing Technology and Business University:Natural Science Edition

基　　金：安徽工程大学研究生教学改革与研究重点项目(2021JYXM001).

摘　　要：目的针对磁悬浮球系统非线性不稳定和滞后性的问题,提出一种基于粒子群优化的自适应灰色预测PID(Proportion Integration Differentiation)复合控制策略。方法通过在PID控制模块的反馈环中引入具有等维新息特征的灰色预测器,对系统误差进行及时反馈修正,以提高控制系统的响应速度和鲁棒性;同时,融合粒子群智能算法对控制器参数迭代优化,以提高控制系统控制精度和抗干扰能力;最后,在MATLAB/Simulink环境下搭建仿真平台进行对比实验。结果验证基于粒子群优化的自适应灰预测控制系统模型的超调量、峰值时间、调节时间显著改善。结论证实该策略可以有效抑制系统滞后性,具有良好的稳定性和鲁棒性。Objective Aiming at the problem of nonlinear instability and hysteresis of the magnetic levitation ball system,an adaptive gray prediction composite control strategy based on particle swarm optimization was proposed.Methods A grey predictor with equal-dimension and new-info characteristics was introduced into the feedback loop of the PID control module to provide timely feedback correction of system errors,so as to improve the response speed and robustness of the control system.And the particle swarm intelligence algorithm was integrated to iteratively optimize the controller parameters,so as to improve the control accuracy and anti-interference ability of the control system.Finally,a simulation platform was constructed in the MATLAB/Simulink environment for comparative experiments.Results The experimental results showed that the overshoot,peak time,and adjustment time of the adaptive grey predictive control system model based on particle swarm optimization were significantly improved.Conclusion It is confirmed that this strategy can effectively suppress the system hysteresis,and has good stability and robustness.

关 键 词：磁悬浮 粒子群算法 灰色预测 PID 自适应 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]