A periodic adaptive learning control method for the medium-speed maglev train with input saturation

In order to improve the operation control performance of medium-speed maglev trains, in this paper, considering the periodicity of trains under repetitive operation on the fixed line and the adverse effects of controller input saturation, a periodic adaptive learning control method under controller input saturation (PALC-IS) is proposed. The controller consists of four parts: PD component, speed feedforward component, periodic adaptive learning control (PALC) component and input saturation component. The PALC component estimates and compensates operation resistance through periodic learning. The input saturation component eliminates the adverse effects of input saturation on system performance. The simulation results show that the operation control method proposed in this paper can effectively improve the operation control performance of medium-speed maglev trains.