Nonlinear Optimal Control of Induction Motors for Maglev Trains Propulsion

Abstract

Propulsion in Maglev trains can be implemented with the use of linear induction motors. The control of such induction motors is a non-trivial problem and its solution is imperative for achieving the reliable and precise functioning of Maglev trains under uncertain and variable load. In this article a nonlinear optimal (H-infinity) control approach is proposed for the propulsion of Maglev trains. The dynamic model of the induction motor undergoes approximate linearization at each iteration of the control algorithm around a temporary operating point (equilibrium). The linearization is based on first-order Taylor series expansion and on the computation of the system's Jacobian matrices. For the approximately linearized model of the induction motor an optimal (H-infinity) feedback controller is designed. This controller provides the solution to the optimal control problem of the motor under model uncertainty and external perturbations. The computation of the controller's feedback gain requires the solution of an algebraic Riccati equation taking place at each time-step of the control method. The stability properties of the control scheme are proven through Lyapunov analysis. It is demonstrated that the motor's control loop satisfies the H-infinity tracking performance criterion which in turn signifies elevated robustness against model uncertainty and external perturbations. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven.