Neural network-based adaptive super-twisting sliding mode control for hybrid magnetic levitation system with external disturbance

Hybrid magnetic levitation system compared with pure electromagnetic levitation system, because of the introduction of permanent magnets, the hybrid levitation system is more sensitive to parameter uncertainty and external disturbance. The traditional control strategy is unable to meet the operational requirements of the hybrid levitation system. To solve this problem, an adaptive super-twisting sliding mode controller based on a neural network is designed to address unknown parameters and external disturbance. Firstly, this study analyses the impact of the introduction of permanent magnets on the controllability and safety of the system. An adaptive sliding mode controller is designed. To address the issue of model parameter uncertainty, a neural network is employed to fit the unknown quantities. Then, to further solve the chattering issue on the platform in the face of disturbances, an adaptive super-twisting controller was developed and designed based on the neural network controller. Finally, related experimental verification was carried out on a hybrid levitation experimental platform. The experimental results indicate that the proposed control strategy is able to maintain stable levitation of the platform even under external disturbance and ensure the airgap tracking and levitation safety of the system.