A novel integral terminal non-homogeneous super-twisting sliding mode speed control of the PMSM in machine tools based on an iterative learning disturbance observer
Abstract This study presents a novel control strategy to address the problem of speed tracking control of permanent magnet synchronous motors (PMSMs) for machine tools. It combines a P-type iterative learning disturbance observer (PILDO) and a superior integral terminal non-homogeneous super-twisting sliding mode controller. First, the PMSM mathematical model is established with an unknown disturbance. Then, a PILDO is proposed to estimate the unknown disturbances of the system and compensate for them through feedforward compensation. This addresses the problem that both internal and external uncertainties can lead to the degradation of motor control performance. To improve the dynamic and steady-state performance of the speed control system, an improved integral terminal sliding mode controller is proposed, and an improved non-homogeneous super-twisting algorithm is used as the switching control law. Finally, the stability and convergence of the system are ultimately demonstrated by the Lyapunov theorem. The simulation results demonstrate that the control method can successfully reduce chattering while improving the robustness, speed tracking performance and anti-interference capabilities of the system.
Authors
- Hui Jiang (ORCID: https://orcid.org/0000-0002-7605-5951)
- Yinfeng Liu (ORCID: https://orcid.org/0000-0002-1427-4462)
- Zheng Minli (ORCID: https://orcid.org/0000-0002-5701-7271)
- Mingjia Zhou
- Lin Yang (ORCID: https://orcid.org/0000-0002-5068-4426)
- Jun Fang (ORCID: https://orcid.org/0000-0003-3302-671X)
Institutions
- Harbin University of Science and Technology (CN)
- Qiqihar University (CN)
- Harbin Engineering University (CN)
- Harbin Huade University (CN)
- Qiqihar Medical University (CN)
Publication Details
- Journal
- Measurement Science and Technology
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1088/1361-6501/ae09c4
- Citations
- 1
- Primary Topic
- Advanced machining processes and optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.71