Nonlinear Adaptive Backstepping Control-Based Dynamic Recurrent RBFN Uncertainty Observer for High-Speed Micro Permanent-Magnet Synchronous Motor Drive System

Abstract

In this paper, a nonlinear adaptive backstepping control system (NABCS) using dynamic recurrent radial basis function network (DRRBFN) uncertainty observer is proposed to achieve high dynamic performance for high-speed micro permanent-magnet synchronous motor (PMSM) drive. The NABCS incorporates an ideal backstepping controller (IBC), a DRRBFN uncertainty observer and a robust controller. The IBC is designed based on the sense of Lyapunov stability theorem. However, particular information about the uncertainties is required in the backstepping control law so that the performance cannot be influenced seriously. Therefore, an adaptive DRRBFN uncertainty observer is designed to adaptively estimate the non-linear uncertainties online. In addition, the robust controller is designed to recover the residual of the approximation errors of the DRRBFN. Furthermore, the online adaptive control laws are derived based on the Lyapunov stability analysis; so that the stability of the NABCS can be guaranteed. The experimental results confirm the superiority of the proposed NABCS.