A unified control method combined with improved TSF and LADRC for SRMs Using modified grey wolf optimization algorithm. (December 2022)

• Record Type: Journal Article
• Authors: Sun, Xiaodong; Xiong, Yefei; Yao, Ming; Tang, Xingtao; Tian, Xiang
• Is Part Of: ISA transactions. Volume 131(2022)
• Page Start: 662
• Page End: 671
• Publication Date: 2022-12
• Subjects: Torque sharing function -- Linear active disturbance rejection control (LADRC) -- An improved grey wolf optimization algorithm (IGWOA) -- Switched reluctance motor (SRM); Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2022.05.013
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper proposes a unified control method based on an improved grey wolf optimization algorithm to improve the performance of the switched reluctance motors (SRMs) to cope with various operating conditions. Compared with the single control mode of traditional SRMs, an SRM with 12/10 poles is called a multimode switched reluctance motor, which can be used not only as a six-phase motor but also as a three-phase motor. The control method proposed is based on a 12/10 pole SRMs. In the TSRM mode, an improved torque sharing function with parameters is utilized to reduce torque ripple and widen the speed range. In the SSRM mode, the linear active disturbance rejection control (LADRC) method is used to increase the anti-interference ability of the system. For the switching modes, an improved gray wolf optimization algorithm is designed to ensure smooth switching. The global optimal solution is obtained by introducing the coyote group to avoid falling into the local optimal solution. Finally, the experimental results prove the effectiveness of the control method. Highlights: The 12/10 SRM is used as a TSRM mode and a SSRM mode. And a unified control method is proposed. The proposed method uses an improved TSF and the LADRC method in different modes and they can switch smoothly. In TSRM mode, an improved TSF with tunable parameter is proposed to reduce the torque in a wide speed range. An improved grey wolf packs optimization algorithm for introducing coyote packs is designed. The proposed method can reduce torque ripple within 23.5% in the rated speed range.