Advance Control Technique for Magnetic Levitation Train

Abstract

The thesis is based on the design of advanced control techniques for Maglev system, the key purpose of this research is to plan an effective control strategy to deal with nonlinear dynamics of Maglev system, that includes the control of air gap, magnetic flux, and momentum. The control strategy that has been investigated is linear model predictive control (MPC) and nonlinear model predictive control (MPC) whereas a comparison with nonlinear techniques such as backstepping and integral backstepping has been made. The linear model predictive control (MPC) and Nonlinear model predictive control (MPC) has been implemented in the MATLAB’s MPC toolbox for which the linear MPC is applied after the linearizing the Maglev system whereas nonlinear model predictive control (MPC) uses the nonlinear model of Maglev system. Simulation results for nonlinear model predictive control (MPC) provides a better result in terms of stability, reduced oscillation, and response time whereas as comparison with linear MPC and nonlinear techniques such as integral backstepping. The thesis concludes that nonlinear model predictive control is the most robust and effective control strategy for Maglev system that handles the nonlinearities and complex behavior of system. Future work can be focused on the adaptive control strategy, real world hardware implementation of Maglev system as to test the performance of controller and computational optimization technique that can reduces the computational power of model predictive control (MPC).