Continuous Time Model Predictive Control for a Magnetic Bearing System

Abstract— The nature of active magnetic bearings has many advantages over the conventional bearing, as its operation is energy efficient and potentially leads to cleaner and noise-free environment. However, the successful operation of an active magnetic bearing system requires a complex real-time control system, because of its unstable characteristics, as well as its nature of being a multi-input and multi-output system. This paper presents design and implementation of a continuous time model predictive control algorithm (CMPC) to an active magnetic bearing system (AMB). In this application, the plant continuous time model is identified from experimental data using prediction error method. The control performance of this algorithm is studied using an experimental AMB laboratory system. A host-target development environment of real-time digital control system with hardware in the loop (HIL) is implemented and demonstrated by controlling a nonlinear, open-loop unstable, and multivariable magnetic levitation device.