磁悬浮系统自抗扰广义预测控制

Active disturbance rejection generalized predictive control for magnetic levitation system

摘要:

针对负载扰动以及轨道“台阶”现象可能导致磁浮列车失稳的问题,将自抗扰广义预测控制（LADRC-GPC）理论引入磁浮列车悬浮系统,设计了一种新型悬浮控制器。控制器采用分层控制策略,内层利用扩张状态观测器(ESO)对系统进行动态补偿,得到被控对象的受控自回归滑动平均模型(CARMA) ,降低了对被控对象数学模型的依赖程度。外层以内层控制为被控对象,采用广义预测控制（GPC）对系统进行动态优化控制,提高了控制器的跟踪性。通过仿真和实验与PID控制算法、线性自抗扰控制（LADRC）算法比较,结果表明:自抗扰广义预测控制算法具有较好的跟踪性与鲁棒性,在较大负载扰动时仍能保持较小的误差。

Abstract:

Considering the problem that the load disturbance and track “step” phenomenon may lead to instability of maglev trains, the active disturbance rejection generalized predictive control (LADRC-GPC) theory was introduced into the levitation system of maglev train, and a new suspension controller was designed. The controller adopted a layered control strategy, and the extended state observer (ESO) was used to dynamically compensate the system in the inner layer. The controlled autoregressive moving average model (CARMA) of the controlled object was obtained to reduce the dependence on the mathematical model of the controlled object. The inner layer was taken as the controlled object by the outer layer, and the generalized predictive control (GPC) was used to dynamically optimize the control system, which improved the tracking performance of the controller. By comparing with PID control algorithm and linear active disturbance rejection control (LADRC) algorithm through simulation and experiment, results show that the LADRC-GPC algorithm had better tracking performance and robustness, and could maintain small error under large load disturbance.