Adaptive Optimal Tracking Control Via Actor-Critic-Identifier Based Adaptive Dynamic Programming for Permanent-Magnet Synchronous Motor Drive System

This article presents a robust adaptive optimal tracking control (RAOTC) scheme for permanent-magnet synchronous motor (PMSM) servo drive with uncertain dynamics via adaptive dynamic programming (ADP) method. First, an adaptive identifier is developed to estimate the nonlinear dynamic functions of the PMSM using a functional-link neural-network. Then, the proposed RAOTC scheme is developed that combines an adaptive steady-state controller, an adaptive optimal tracking controller, and a robust controller. The adaptive steady-state controller is designed for attaining the targeted tracking response at the steady-state using the estimated nonlinear dynamics. The adaptive optimal tracking controller is designed for stabilizing the tracking error dynamics at the transient state in an optimal manner. Further, the robust controller is developed for compensating the approximation errors of neural-networks introduced by implementing the ADP technique. Accordingly, actor and critic neural-networks are employed for facilitating the online solution of the Hamilton-Jacobi-Bellman equation for approximating the adaptive optimal control (OC) laws via ADP method. Based on Lyapunov approach, the closed-loop stability of the PMSM servo drive system is proved to demonstrate that the proposed RAOTC scheme can ensure the system state tracking the targeted trajectory effectively. The proposed RAOTC scheme validation is performed via experimental analysis. From the experimental validation results, the PMSM servo drives dynamic behavior using the proposed RAOTC scheme can attain the robust and OC performance regardless the compounded disturbances and parameter uncertainties.