基于PSO磁悬浮球系统自适应灰预测控制

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。

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

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.