Trajectory Tracking Control Based on NP-RMPC With the Uncertain Model Parameters for Magnetic Bearing-Rotor System

The model mismatch and parameter uncertainty of flywheel battery system are the key factors that affect the stable operation of magnetic suspension flywheel battery system under the external random interference of vehicle. The existing control methods usually ignore the uncertain effects of the system. In this study, a nominal particle robust model predictive control (NP-RMPC) method for nonlinear systems considering the uncertainty of model parameters is proposed. First, the particle characteristics are used to model the system uncertainty and observable system dynamics. Then, a robust observer is constructed to estimate the system uncertainty caused by external random perturbations. Then, based on the nominal particle prediction model, the robust model predictive control (RMPC) is optimized, and an off-line fast solution method is designed for efficient output control law. Finally, various experiments are carried out on the flywheel rotor trajectory tracking control. The results show that the response time is reduced by 60% and the tracking error is reduced by 66.94%. Consequently, the effectiveness of this method is proved.