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.
Highlights
Permanent magnet synchronous motor (PMSM) has become the mainstream motor in the fields of active aircraft, electric vehicles and industrial servo drives due to its high torque density, high power density, and high efficiency
Based on the locally properties of radial basis function (RBF) neural network, we describe Eq (9) and Eq (10) in the following form: ei
It can be seen that from the time 0 to 0.2 seconds, the motor velocity is affected by noise and various uncertainties, which is different from the situation in the simulation
Summary
Permanent magnet synchronous motor (PMSM) has become the mainstream motor in the fields of active aircraft, electric vehicles and industrial servo drives due to its high torque density, high power density, and high efficiency. It is a typical nonlinear complex system with the general properties of chaotic systems, showing self-similarity, initial value sensitivity, and signal pseudo-random complexity [1]. The most common uncertainty is the electrical parameters in the motor model [2] In actual control, these variations in electrical parameters will cause inaccurate estimation of various observers, failure of shaft decoupling, control performance, the dynamic and static modeling quality reduction, and even affecting the stability of the motor control system [3]. To accurately identify the parameters of the PMSM to get an accurate motor model and improve the control performance, a variety of PMSM parameter identification methods have appeared, such as model reference adaptive (MRAS) method, state observer method, intelligent identification method and so on
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