Hybrid recurrent cerebellar model articulation controller-based supervisory H^∞ motion control system for permanent-magnet synchronous motor servo drive

This study proposes a hybrid supervisory H^∞ control system using a recurrent cerebellar model articulation controller (RCMAC) for achieving high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drive. The hybrid control system is composed of a RCMAC, a supervisory controller and a robust H^∞ controller. The RCMAC is used as the main position tracking controller to mimic a perfect computed torque control law, and the robust H^∞ controller is designed with adaptive bound estimation algorithm to compensate for the approximation error between the RCMAC and the ideal controller. The supervisory controller is designed based on the uncertainty bound of the PMSM to stabilise the system states around a predefined bound region and achieve satisfactory tracking performance. The online adaptive control laws are derived based on the Lyapunov stability analysis, so that the stability of the system can be guaranteed. A computer simulation is developed and an experimental system is established to validate the effectiveness of the proposed hybrid control system. All control algorithms are implemented in a TMS320C31 digital signal processor-based control computer. The simulated and experimental results confirm that the proposed hybrid control system grants robust performance and precise response regardless of load disturbances and PMSM parameters uncertainties.