Learning Adaptive Sliding Mode Control for Repetitive Motion Tasks in Maglev Rotary Table

Abstract

Considering the increasingly strict motion precision requirements and repetitive task characteristics of magnetic levitation systems (MLSs) in the sophisticated industry, this article proposes a novel learning adaptive sliding mode control (LASMC) strategy for the MLS to achieve an excellent tracking performance. The LASMC scheme is obtained by combining adaptive sliding mode control (ASMC) and iterative learning control (ILC) terms in a parallel structure. ASMC can guarantee system stability and strong robustness, which employs an adaptive switching gain and parameter adaption algorithm. Even if the accurate disturbance bounds of MLS are not known, the ASMC item can also adjust the switching control gain online to ensure the stability and robustness of the system. Additionally, the ILC term can further improve the MLS performance for repetitive motion tasks without the accurate dynamics model. The asymptotic stability of the LASMC strategy is verified based on the Lyapunov theorem in the presence of unmodeled dynamics, disturbances, and saturation. Comparative experiments carried out on a maglev rotary table demonstrate that the proposed control strategy achieves excellent tracking accuracy and disturbance robustness in the practical MLS. The LASMC scheme provides a decent idea for the application of intelligent control technology in the controller design of MLSs.