Design of backstepping fuzzy-neural-network control for hybrid maglev transportation system

In this study, a backstepping fuzzy-neural-network control (BFNNC) is designed for the on-line levitated balancing and propulsive positioning of a hybrid magnetic-levitation (maglev) transportation system. In the proposed BFNNC scheme, a fuzzy neural network (FNN) control is utilized to be the major control role by imitating a backstepping control (BSC) strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control (BSPSOC) system in previous research.