Your browser doesn't support javascript.
loading
A composite sliding mode control with the first-order differentiator and sliding mode observer for permanent magnet synchronous machine.
Hong, Junjie; Lin, Xijian; Zhang, Jianbo; Huang, Wei; Yan, Baiping; Li, Xiyu.
Afiliação
  • Hong J; School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Lin X; School of Automation, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: chase_lxj@163.com.
  • Zhang J; School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Huang W; School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Yan B; School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Li X; School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
ISA Trans ; 147: 489-500, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38395719
ABSTRACT
This paper proposes a composite sliding mode control (SMC) to optimize the tracking performance and the anti-disturbance performance of permanent magnet synchronous machine (PMSM) speed regulation systems. The differential term in the control law can magnify the measurement noise, resulting in more discontinuity. To filter out the high frequency noise and make the control law smoother, the first-order differentiator (FOD) is employed to estimate the speed error and its derivative. Since the feedforward compensation can improve the robustness of the system, a disturbance observer (DOB) based on the sliding mode observer (SMO) is designed to reinforce the dynamic performance under disturbance variation. Under the effect of the feedforward compensation, chattering can be further weakened by decreasing the switching gain appropriately. Finally, the effectiveness of the proposed methods is confirmed by various experimental results.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article