Observer-based adaptive fuzzy-neural-network control for hybrid maglev transportation system

Abstract

This study focuses on the design of an observer-based adaptive fuzzy-neural-network control (OAFNNC) for real-time levitated balancing and propulsive positioning of a hybrid magnetic-levitation (maglev) transportation system with only position state feedback. The dynamic model of the hybrid maglev transportation system, including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics, is firstly constructed. Then, an adaptive observer is designed to estimate velocity signals for the later control utilization, and an adaptive observer and control (AOC) scheme is formed by means of the stability analyses of the entire system. The ultimate goal is to design an on-line fuzzy-neural-network (FNN) velocity-sensorless control methodology to cope with the problem of the complicated control transformation and the requirement of detailed system parameters in the AOC scheme, and to directly ensure the stability of the entire system without the requirement of strict constraints, detailed system information and auxiliary compensated controllers despite the existence of uncertainties. In the proposed OAFNNC scheme, a FNN control is utilized to be the major control role by imitating the AOC 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 OAFNNC scheme is indicated in comparison with the AOC strategy and the backstepping particle-swarm-optimization control (BSPSOC) system in previous research.