Deadbeat Predictive Speed Control of PMSM via Enhanced Low-Gain Disturbance Observer Design and Inertia Identification
In this study, an enhanced low-gain disturbance observer is put forward to achieve high load rejection and parameters robustness for deadbeat predictive speed control (DPSC) of the permanent magnet synchronous motor (PMSM) system. The proposed observer preserves the main feature of standard low-gain and low-bandwidth observers in terms of strong suppression to measurement noise and peaking-free while overcoming their main drawback, namely the “slow convergence.” First, the mechanical dynamic model of the PMSM is built, and the conventional DPSC scheme based on extended state observer (ESO) is then presented and analyzed. Second, to reduce the observer bandwidth, a novel adaptive ESO (AESO) with inertia identification is designed based on Lyapunov’s stability theory. The AESO is further enhanced by the predictive error model (PEM) to improve the convergence speed during the transient, where the stability and the fast convergence are proved rigorously. In consequence, an overall combination of the DPSC scheme and the proposed PEM-AESO is developed rationally. Finally, the experimental results validate the remarkable robust performance of the proposed control method for the PMSM in the sense that, compared with the ESO-based DPSC, the recovery time and the overshoot of the proposed method are reduced by 28% and 67%, respectively.
Authors
- Deqing Huang (ORCID: https://orcid.org/0000-0002-8185-9030)
- Ruiqi Li (ORCID: https://orcid.org/0009-0000-4332-8485)
- Qiyuan Zhao
- Yong Chen (ORCID: https://orcid.org/0009-0009-4282-4191)
Institutions
- Southwest Jiaotong University (CN)
Publication Details
- Journal
- IEEE Journal of Emerging and Selected Topics in Power Electronics
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1109/jestpe.2024.3507798
- Citations
- 6
- Primary Topic
- Iterative Learning Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.97
Funders
- National Natural Science Foundation of China