Dynamic control of maglev transportation system via adaptive fuzzy-neural-network

This study designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating a sliding-mode control (SMC) strategy for a magnetic-levitation (maglev) transportation system. In the model-free AFNNC, on-line learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations.