Adaptive Fast Super-Twisting Sliding Mode Direct Thrust Control for Linear Induction Machine Adapted to Linear Metro
This article proposes an adaptive fast super-twisting (AFST) sliding mode control (SMC) strategy based on direct thrust control (DTC), referred to as AFST-DTC, for linear induction machine (LIM) drive systems used in linear metro applications. First, the AFST technique is designed and implemented on LIM to achieve rapid response, precise speed tracking, and robustness against load disturbances. The proposed AFST-DTC method demonstrates fast convergence when the sliding mode state is near or far from the designed sliding mode surface, with reduced chattering phenomenon compared to the conventional super-twisting under DTC (ST-DTC) method. Second, the convergence time is calculated, and the stability of the control system is verified using the Lyapunov function. Finally, comprehensive simulation and experimental results on a prototyped 3 kW arc induction machine (AIM) have fully demonstrated the effectiveness and advantages of the AFST control method, including faster transient response, better steady-state and dynamic performance, and lower thrust and flux ripple, compared to those of ST-DTC and conventional DTC (CDTC), respectively.
Authors
- Jian Ge (ORCID: https://orcid.org/0000-0002-8735-8659)
- Yirong Tang (ORCID: https://orcid.org/0000-0002-0624-0916)
- Abdul Khalique Junejo (ORCID: https://orcid.org/0000-0002-4887-516X)
- Md. Rabiul Islam (ORCID: https://orcid.org/0000-0003-3133-9333)
- Wei Xu (ORCID: https://orcid.org/0000-0003-0816-6016)
Institutions
- Chinese Academy of Sciences (CN)
- University of Wollongong (AU)
- Institute of Electrical Engineering (CN)
- Institute of Process Engineering (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Transportation Electrification
- Published
- 2025-08-08
- DOI
- https://doi.org/10.1109/tte.2025.3597129
- Citations
- 1
- Primary Topic
- Electric Motor Design and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Natural Science Foundation of China