Literature DB >> 30563689

Anti-disturbance speed control of low-speed high-torque PMSM based on second-order non-singular terminal sliding mode load observer.

En Lu1, Wei Li2, Xuefeng Yang3, Yufei Liu4.   

Abstract

This paper presents an anti-disturbance speed control of low-speed high-torque permanent magnet synchronous motor (PMSM) based on the second-order non-singular terminal sliding mode load observer. According to the coordinate transformation theory, the mathematical model of PMSM is established. Subsequently, the second-order non-singular terminal sliding mode observer (SNTSMO) is designed to observe the changes of load disturbance in the PMSM system. The SNTSMO combines the advantages of both high-order sliding mode and non-singular terminal sliding mode to achieve the fast convergence and no chattering. Next, the sliding mode controller (SMC) is designed to achieve speed loop control of PMSM. Then, the anti-disturbance compound speed controller is established on the basis of SMC and SNTSMO, wherein the feed-forward compensation is used to reduce the disturbance from the load. Finally, the numerical simulations and experiments are presented according to the schematic diagram of the designed compound speed controller of PMSM. The results demonstrate that the designed SNTSMO can precisely estimate the load disturbance and suppress the effects of buffeting in the traditional sliding mode observer (SMO). Additionally, the designed compound speed controller of PMSM can achieve smooth speed control in the presence of load disturbance, achieve the purpose of anti-disturbance speed control and further improve the robustness of the control system.
Copyright © 2018. Published by Elsevier Ltd.

Keywords:  Anti-disturbance speed control; Load observer; Non-singular terminal sliding mode; PMSM; Second-order sliding mode

Year:  2018        PMID: 30563689     DOI: 10.1016/j.isatra.2018.11.028

Source DB:  PubMed          Journal:  ISA Trans        ISSN: 0019-0578            Impact factor:   5.468


  1 in total

1.  RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer.

Authors:  Ye Li; Dazhi Wang; Mingtian Du; Shuai Zhou; Shuo Cao; Yanming Li
Journal:  Comput Intell Neurosci       Date:  2022-08-22
  1 in total

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