On-Line Supervisory Control Design for Maglev Transportation System via Total Sliding-Mode Approach and Particle Swarm Optimization

Abstract

This study focuses on the design of an on-line levitation and propulsion control for a magnetic-levitation (maglev) transportation system. First, the dynamic model of a maglev transportation system including levitated electromagnets driven by linear servo amplifiers and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed. Then, a total sliding-mode (TS) control strategy is introduced, and the concept of TS control is incorporated into particle swarm optimization (PSO) to form an on-line TSPSO control framework with varied inertial weights for preserving the robust control characteristics and reducing the chattering control phenomena of TS control. In this TSPSO control scheme, a PSO control system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TS control without strict constraint and detailed system knowledge. In order to further directly stabilize the system states around a predefined bound region and effectively accelerate the searching speed of the PSO control, a supervisory mechanism is embedded into the TSPSO control to constitute a supervisory TSPSO (STSPSO) control strategy. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations and experimental results, and the superiority of the STSPSO control scheme is indicated in comparison with the adaptive fuzzy neural network, PSO-based proportional-integral-differential, TS and TSPSO control strategies.