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).
