Purpose

The purpose of this paper is to present a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor.

Design/methodology/approach

A novel recurrent radial basis function network (RBFN) is used to is used to approximate unknown nonlinear functions in permanent magnet synchronous motor (PMSM) dynamics. Then, using the functions obtained from the neural network, it is possible to design a model-based and precise controller for PMSM using the immersive modeling method.

Findings

Experimental results indicate the appropriate performance of the proposed method.

Originality/value

This paper presents a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. A novel recurrent RBFN is used to is used to approximate unknown nonlinear functions in PMSM dynamics.

中文翻译：

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基于智能滑模技术的PMSM速度控制

目的

本文的目的是为实验永磁同步电动机提供一种新颖的智能反步滑模控制。

设计/方法/方法

一种新颖的递归径向基函数网络（RBFN）用于逼近永磁同步电动机（PMSM）动力学中的未知非线性函数。然后，使用从神经网络获得的功能，可以使用沉浸式建模方法为PMSM设计基于模型的精确控制器。

发现

实验结果表明了该方法的适当性能。

创意/价值

本文提出了一种新型的智能型反步滑模控制实验永磁同步电动机。一种新颖的递归RBFN用于在PMSM动力学中近似未知的非线性函数。