Error-Bounded Tracking of Maglev Planar Motor Based on Robust Model Predictive Control

This article presents a robust model predictive control (MPC) scheme for error-bounded tracking of the magnetically levitated (maglev) planar motor. The motivation lies in bringing the machining error within the desired tolerance zone. An augmented system containing the dynamics of measured position, estimated state, and estimated disturbances is used as the prediction model of MPC, such that the obtained optimal control signals can remove the positioning errors in steady state caused by the disturbances. Based on the augmented system, reference dynamics and constraints, a robust control invariant (RCI) set is derived using an iterative procedure, which can be terminated in finite steps. The RCI set is explicitly incorporated into the MPC formulation for error-bounded tracking. The proposed method is successfully implemented on the maglev positioning system, and systematic experiments are given to illustrate that tracking errors are bounded in the defined region.