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Gonzalez-Prieto, A, Gonzalez-Prieto, I, Dordevic, O, Aciego, JJ, Montenegro, J, Duran, MJ and Khan, MU (2023) Memory-based Model Predictive Control for Parameter Detuning in Multiphase Electric Machines. IEEE Transactions on Power Electronics, 39 (2). 2546 -2557. ISSN 0885-8993
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Memory based Model Predictive Control for Parameter Detuning in Multiphase Electric Machines.pdf - Accepted Version Download (2MB) | Preview |
Abstract
Model predictive control (MPC) is a popular control technique to regulate multiphase electric drives (ED). Despite the well-known advantages of MPC, it is sensitive to parameter detuning and lacks the capability to eliminate steady-state errors. The appearance of an offset between the reference and measured currents can significantly jeopardize the performance of the electric drive. This work suggests the use of a memory-based model predictive control (MB-MPC) that activates a compensation term when the parameter mismatch is detected. The suggested MB-MPC is universal for any multiphase machine if spatial harmonics are neglected since the proposed method does not consider any of the secondary x-y planes. Experimental results in two different rigs with six- and nine-phase induction motors prove this universality as well as its capability to eliminate current and speed offsets.
| Item Type: | Article |
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| Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
| Uncontrolled Keywords: | 0906 Electrical and Electronic Engineering; Electrical & Electronic Engineering |
| Subjects: | T Technology > TK Electrical engineering. Electronics. Nuclear engineering |
| Divisions: | Engineering |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 30 Nov 2023 11:39 |
| Last Modified: | 31 Oct 2024 15:43 |
| DOI or ID number: | 10.1109/tpel.2023.3328427 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/21989 |
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