Multiple-Mode Antidisturbance Model Predictive Control of VLF-PMSM Sensorless Drive System With Double-Decoupling Position Search Observer
For the sensorless drive system of the variable-leakage-flux permanent-magnet synchronous motor (VLF-PMSM), its obvious parameter variation and additional harmonic interference greatly cause the electrical signal distortion and affect the estimated accuracy of rotor position, which will not meet the requirement of multioperating conditions for electric vehicles (EVs). To overcome this problem, a multiple-mode antidisturbance model predictive current control (ATMPCC) strategy based on a double-decoupling position search observer (DPO) is proposed from the perspectives of electrical signal error analysis and disturbance compensation. By innovatively introducing a state variable and a mode switching to redesign the predictive model, the ATMPCC strategy can improve the ability to resist parameter variation and harmonic interference. Furthermore, by improving the dichotomy-iteration-based observer, the DPO, implementing a double-decoupling process of back EMF, can effectively reduce the estimated position error. Owing to the redesign of the predictive model and the double-decoupling of the back EMF, the issues of parameter variation and additional harmonic interference can be simultaneously overcome, which can meet the requirement of multiple operating conditions. Finally, the correctness and effectiveness of the proposed strategy are verified by experimental results.
Authors
- Yican Zhou (ORCID: https://orcid.org/0009-0001-2474-785X)
- Xiaoyong Zhu (ORCID: https://orcid.org/0000-0001-6333-3329)
- Li Zhang (ORCID: https://orcid.org/0000-0002-5308-8988)
- Wen‐Hua Chen (ORCID: https://orcid.org/0000-0003-3356-2889)
- Fuyao Tan
Institutions
- Jiangsu University (CN)
Publication Details
- Journal
- IEEE Transactions on Industrial Electronics
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1109/tie.2024.3451059
- Citations
- 2
- Primary Topic
- Advanced Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.66