Direct Model Predictive Control of Synchronous Reluctance Motor Drives

Riccio, Jacopo; Karamanakos, Petros; Odhano, Shafiq; Tang, Mi; Nardo, Mauro Di; Zanchetta, Pericle

Abstract

This paper investigates a finite-control set model-predictive control (FCS-MPC) algorithm to enhance the performance of a synchronous reluctance machine drive. Particular emphasis is placed on the definition of the cost function enabling a computationally light implementation while targeting good transient and steady-state performance. In particular, this work proposes the inclusion of an integral term into the cost function to ensure zero steady-state errors thus compensating for any model inaccuracies. A control effort term is also considered in the formulation of the cost function to achieve a high ratio between the sampling frequency and the average switching frequency. After a comprehensive simulation study showing the advantages of the proposed approach over the conventional FCS-MPC for a wide range of operating conditions, several experimental test results are reported. The effectiveness of the proposed control approach, including a detailed analysis of the effect of the load and speed variations, is thus fully verified providing useful guidelines for the design of a direct model predictive controller of synchronous reluctance motor drives.

Citation

Riccio, J., Karamanakos, P., Odhano, S., Tang, M., Nardo, M. D., & Zanchetta, P. (2023). Direct Model Predictive Control of Synchronous Reluctance Motor Drives. IEEE Transactions on Industry Applications, 59(1), 1054-1063. https://doi.org/10.1109/TIA.2022.3213002

• Journal Article Type: Article
• Acceptance Date: Sep 9, 2022
• Online Publication Date: Oct 10, 2022
• Publication Date: 2023-01
• Deposit Date: Nov 3, 2022
• Publicly Available Date: Nov 3, 2022
• Journal: IEEE Transactions on Industry Applications
• Print ISSN: 0093-9994
• Electronic ISSN: 1939-9367
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 59
• Issue: 1
• Pages: 1054-1063
• DOI: https://doi.org/10.1109/TIA.2022.3213002
• Keywords: Electrical and Electronic Engineering; Industrial and Manufacturing Engineering; Control and Systems Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/12330014
• Publisher URL: https://ieeexplore.ieee.org/document/9914669
• Additional Information: © 2022 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.