Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer

Lingfei XIAO, Leiming MA, Xinhao HUANG

Front. Inform. Technol. Electron. Eng ›› 2022, Vol. 23 ›› Issue (2) : 328-338.

PDF(3340 KB)
PDF(3340 KB)
Front. Inform. Technol. Electron. Eng ›› 2022, Vol. 23 ›› Issue (2) : 328-338. DOI: 10.1631/FITEE.2000317
Orginal Article
Orginal Article

Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer

Author information +
History +

Abstract

In this paper, an intelligent fractional-order integral sliding mode control (FOISMC) strategy based on an improved cascade observer is proposed. First, an FOISMC strategy is designed to control a permanent magnet synchronous motor. It has good tracking performance, is strongly robust, and can effectively reduce chattering. The proposed FOISMC strategy associates strong points of the integral action (which can eliminate steady-state tracking errors) and the fractional calculus (which is flexible). Second, an improved cascade observer is proposed to detect the rotor information with a smaller observation error. The proposed observer combines an adaptive sliding mode observer and an extended high-gain observer. In addition, an improved variable-speed grey wolf optimization algorithm is designed to enhance controller parameters. The effectiveness of the strategy is tested using simulations and an experiment involving model uncertainty and external disturbance.

Keywords

Permanent magnet synchronous motor / Fractional-order integral sliding mode / Optimization algorithm / Sensorless control / Observer

Cite this article

Download citation ▾
Lingfei XIAO, Leiming MA, Xinhao HUANG. Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer. Front. Inform. Technol. Electron. Eng, 2022, 23(2): 328‒338 https://doi.org/10.1631/FITEE.2000317

RIGHTS & PERMISSIONS

2022 Zhejiang University Press
PDF(3340 KB)

Accesses

Citations

Detail

Sections
Recommended

/