Implicit Generalized Predictive Control of an Active Magnetic Bearing System

In this paper, a simple implicit generalized predictive self-tuning control based on Controlled Auto-Regressive Integrated Moving-Average(CARIMA) model for an active magnetic bearing system(AMB) is proposed. The dynamic behaviors of the AMB system depend on the air gaps between the stator and the rotor. In practice, no precise mathematical model can be established because the AMB system is inherently unstable and the relationship between the electromagnetic force and the current in the coils is nonlinear. The proposed control strategy is a new method of remote predictive control, which combines the advantages of various algorithms as a whole and guarantees the stability of the open loop unstable system. With the implicit generalized predictive control algorithm, the model parameters are not need to be identified, and the solving Diophantine equation on-line can be avoided. It can reduce the workload and save time. Finally, the simulation results on the AMB system with the algorithm show the tracking performance and the effectiveness of the implicit generalized predictive self-tuning control.