Model Predictive Control for an Active Magnetic Bearing System

Abstract

Active magnetic bearing (AMB) systems have attracted much attention in the high speed rotating machinery industry. This paper presents an application of discrete-time model predictive control (MPC) subject to input/states constraints to control an AMB system based on linear time-invariant (LTI) model. The main control objectives are to levitate the rotor shaft of the AMB system while tracking a reference trajectory and to reject possible disturbances without violating the input and state constraints. A nonlinear (NL) model of the AMB system is considered; at each sampling instant, a finite horizon MPC problem is solved to compute the optimal control input. The performance and the efficiency of the proposed MPC is validated via simulation and comparison with another classical PID controller.