Model-Free Control Design for Hybrid Magnetic Levitation System

Abstract

This study investigates three model-free control strategies including a simple proportional-integral-differential (PID) scheme, a fuzzy-neural-network (FNN) control and a robust control for a hybrid magnetic levitation (maglev) system. In general, the lumped dynamic model of a hybrid maglev system can be derived by the transforming principle from electrical energy to mechanical energy. In practice, this hybrid maglev system is inherently unstable in the direction of levitation, and the relationships among airgap, current and electromagnetic force are highly nonlinear, therefore, the mathematical model can not be established precisely. In order to cope with the unavailable dynamics, model-free control design is always required to handle the system behaviors. In this study, the experimental comparison of PID, FNN and robust control systems for the hybrid maglev system is reported. From the performance comparison, the robust control system yields superior control performance than PID and FNN control systems. Moreover, it not only has the learning ability similar to FNN control, but also the simple control structure to the PID control