Adaptive neural network control for permanent magnet synchronous motor with input nonlinearity

Abstract

AbstractThis study aims to design a new adaptive control method for permanent magnet synchronous motors (PMSMs) using neural networks (NNs). In comparison to traditional motor backstepping control designs, this research introduces a command filtering strategy to effectively address the common issue of “complexity explosion” in traditional methods. Additionally, considering the potential input hysteresis nonlinearity in practical applications, we introduce a hysteresis inverse operator to mitigate its adverse effects on control. Furthermore, by employing a finite‐time control strategy, we ensure rapid convergence of tracking errors within a finite time frame. Moreover, an adaptive NN controller is designed to approximate unknown continuous nonlinear functions of the system. Finally, the stability and convergence of the closed‐loop system are analyzed using the direct Lyapunov method.