Cascade Control Solutions for Maglev Systems

Abstract

In this paper a cascade control system (CCS) structure made of a combination of tensor product (TP)-based model transformation and of fuzzy control is designed for the position control of the magnetic Ievitation (Maglev) laboratory equipment. The linearized Maglev system model was first stabilized using two control method: a state-feedback control structure (SF-CS) and a Proportional-Integral-state feedback control structure (PISF-CS). A comparative study of these state-feedback CSs is also included. A parallel distributed compensation technique (PDC) is used in the TP-based design of the inner state feedback control loops which was next simplified by simple least squares identification algorithms. In the next step the Takagi-Sugeno (TS) fuzzy controller is designed in the outer control loops using the modulus optimum method and the modal equivalence principle. A comparative analysis and experimental results are given to validate the efficiency of the proposed CCS structure.