Investigation of RBF-SMC Control Strategy for Vertical Dynamics of Maglev Car Considering Temperature Rise Effects

Abstract

To address the levitation force attenuation in a magnetic levitation (maglev) car with a permanent magnet electrodynamic wheel (PMEDW) system caused by the temperature rise of the conductive plate, this paper proposes an adaptive sliding mode control strategy combined with a Radial Basis Function neural network (RBF-SMC). This approach enhances both the levitation stability and anti-interference capability of the maglev car system. Initially, a four-wheel dynamic model is established. The RBF neural network is then introduced to observe and mitigate disturbances caused by the temperature rise. An RBF-SMC control strategy is designed to improve the system's static levitation stability. The effectiveness of this control strategy is evaluated through simulations and experiments under various operating conditions. The research results indicate that, compared to the traditional PID control strategy, the proposed method reduces tracking error by 75.2%, compensates for the levitation force attenuation caused by the eddy current temperature rise of the conductive plate, and suppresses disturbances.