A sliding mode periodic adaptive learning operation control method for medium-speed maglev trains

Air resistance is one of the major disturbances that affect the operation of the medium-speed maglev trains. In this paper, using the periodic characteristics of the maglev trains round-trip operation along the fixed line, an operation control method based on sliding mode periodic adaptive learning control (SM-PALC) is proposed to reduce the position error and improve the robustness of the control systems. The proposed SM-PALC controller consists of four parts: an equivalent PD control part, a speed feed forward part, a magnetic resistance compensation part and a sliding mode periodic adaptive air resistance compensation part. Under the condition that the line parameters are unknown, the sliding mode periodic adaptive learning compensation part learns from the acquired information in the previous period to estimate two unknown parameters in the air resistance and reduces the influence of the air resistance on the train operation. The simulation results show that the proposed SM-PALC operation control method can effectively improve the dynamic performance of the maglev train operation.