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Direct Model Predictive Control of Synchronous Reluctance Motor Drives

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

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Jacopo Riccio

Petros Karamanakos

Shafiq Odhano

Mi Tang


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.


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.

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 (IEEE)
Peer Reviewed Peer Reviewed
Volume 59
Issue 1
Pages 1054-1063
Keywords Electrical and Electronic Engineering; Industrial and Manufacturing Engineering; Control and Systems Engineering
Public URL
Publisher URL
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