Intelligent Fault-Tolerant Control for High-Speed Maglev Transportation Based on Error-Driven Adaptive Fuzzy Online Compensator

Abstract

High-speed maglev transportation is a new intelligent transportation system that combines high speed and eco-friendliness. The intelligent control of high-speed maglev trains (HSMT) faces many challenges such as partial actuator failure, strong nonlinearity, and input constraints ( i.e. unidirectional limitation, saturation, and dead zones). Additionally, racing effect between multiple electromagnets, external disturbances and parameter uncertainty makes it more difficult to maintain precise airgap. This paper presents a novel intelligent fault-tolerant control method to tackle the challenges of levitation control of HSMT under conditions of partial actuator failure, unidirectional inputs, saturation, and dead zones. First, a model of multi-points levitation system is provided. Then, a novel fuzzy-based control method is designed. It is based on an adaptive fuzzy update law and auxiliary manifold surfaces, which utilizes real-time measured data to estimate and compensate for the partial actuator failure and input constraints online. To the best of our knowledge, the developed fuzzy-based control law is the first method for the multi-electromagnet levitation system of HSMT that simultaneously considers partial actuator failure and input constraints. The stability and convergence within finite time of the closed-loop airgap errors are proven through the Lyapunov analysis method. Finally, experimental comparisons between the proposed fuzzy-based method and conventional control methods were conducted. The experimental results show that, compared to the LQR method, the proposed method reduces the system’s maximum overshoot by 93.3% and the maximum steady-state error by 36.98%. Moreover, it maintains stable levitation under actuator failure, which enhances the safety, efficiency, and reliability of future maglev transportation systems.

Topics & Concepts