Modeling and identification of permanent magnet synchronous motor via deterministic learning

Abstract

This paper investigates the identification of a permanent magnet synchronous motor (PMSM) velocity servo system based on deterministic learning theory. Unlike most of the existing studies, this study does not identify the system parameters, but rather the system dynamics. System dynamics is the fundamental knowledge of the PMSM system and contains all the information about the system parameters, various uncertainties, and the system structure. The accurate modeling of the various uncertainties is important to improve the control performance of the controller. In this study, the dynamics of the PMSM system containing various uncertainties are identified based on the system state. Firstly, the system state of the PMSM is measured, and then a suitable RBF neural network is designed based on it. The RBF neural network is used to construct a state estimator that takes the motor system as input. The weights of the RBF neural network are updated using the Lyapunov-based weights. As the weights converge, a constant RBF neural network can be obtained, which contains complete information about the system parameters and the various uncertainties of the motor system. We use the proposed method to identify the simulated and real-time PMSM velocity servo systems separately, and the identification results show the effectiveness and feasibility of the proposed method.