An RBFNN‐Based Direct Inverse Controller for PMSM with Disturbances

Abstract

Considering the system uncertainties, such as parameter changes, modeling error, and external uncertainties, a radial basis function neural network (RBFNN) controller using the direct inverse method with the satisfactory stability for improving universal function approximation ability, convergence, and disturbance attenuation capability is advanced in this paper. The weight adaptation rule of the RBFNN is obtained online by Lyapunov stability analysis method to guarantee the identification and tracking performances. The simulation example for the position tracking control of PMSM is studied to illustrate the effectiveness and the applicability of the proposed RBFNN-based direct inverse control method.

Memoryless radial basis function neural network based proportional integral controller for PMSM drives

“…RBFNN evinces its innate characteristics of simple architecture, fast learning rate, and have better approximation abilities. But, a few attention is exerted to control the speed of PMSM by using this robust neural network algorithm [27], [28] and hence an in depth research is required to exploit the feature of RBFNN. From this inspiration, a memoryless RBFNN speed control method for PMSM is developed in this paper.…”

A Novel Model-Free Intelligent Proportional-Integral Supertwisting Nonlinear Fractional-Order Sliding Mode Control of PMSM Speed Regulation System

“…In this paper, we refer to control strategies of the above as the model-related control strategy. However, due to the existence of various uncertainties in real engineering systems, including parameter uncertainties, incomplete modeling dynamics, and unknown external disturbances, it often poses challenges to the above modelrelated control strategies [10][11][12]. In view of the above challenges and difficulties, many scholars put forward a model-free control strategy based on an ultralocal mode.…”