Robust Optimal Control of High-Speed Permanent-Magnet Synchronous Motor Drives via Self-Constructing Fuzzy Wavelet Neural Network

Abstract

This article presents the design of an adaptive backstepping robust optimal control (ABROC) approach for achieving performance with high dynamic of high-speed micro permanent-magnet synchronous motor (HS-MPMSM) drive. First, a backstepping controller is designed for stabilizing the HS-MPMSM drive. To enhance the performance of the control system against external disturbances and parameter variations, an adaptive backstepping robust controller (ABRC) is developed. The ABRC combines a backstepping controller, an adaptive self-constructing fuzzy wavelet neural network (SCFWNN) identifier, and a robust controller. The proposed identifier is developed to approximate the nonlinear functions online. Furthermore, the robust controller is designed to recover the SCFWNN approximation errors. As the online adaptive control laws are derived via Lyapunov theory, thus, the ABRC stability is assured. To attain the optimal control performance, an infinite horizon optimal controller using a critic neural-network (NN) is developed and combined with ABRC to construct the ABROC approach. The critic NN is developed to approximate the optimal value function of the Hamilton–Jacobi–Bellman equation, which is used to develop the optimal controller. The experimental results are presented to verify the effectiveness of the proposed approach. The results validate that the ABROC approach is robust against parameter uncertainties and external disturbances.