Predictive Torque and Flux Control of an Induction Machine Fed by a Voltage Source Inverter

Rivera, Marco; Riveros, Jose; Rodriguez, Consuelo; Wheeler, Patrick; Rohten, Jaime

Abstract

Model predictive control strategies have recently emerged as a viable alternative for the control of power electronic converters in a range of applications. In this paper a new model predictive control technique for both the ux and torque of an induction machine is presented. The algorithm selects from the available switching states of the voltage source inverter, the chosen state in each sample period optimizes a cost function which is based on the tracking of the electromagnetic torque and the stator ux together with additional criteria to ensure the proper operation of the machine. The results demonstrate the fast torque-ux response with the required quasi sinusoidal stator machine currents.

Citation

Rivera, M., Riveros, J., Rodriguez, C., Wheeler, P., & Rohten, J. (2021, March). Predictive Torque and Flux Control of an Induction Machine Fed by a Voltage Source Inverter. Presented at IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA 2021), Valparaíso, Chile

• Presentation Conference Type: Edited Proceedings
• Conference Name: IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA 2021)
• Start Date: Mar 22, 2021
• End Date: Mar 26, 2021
• Acceptance Date: Jan 24, 2021
• Online Publication Date: Jul 1, 2021
• Publication Date: Mar 22, 2021
• Deposit Date: Oct 29, 2021
• Publicly Available Date: Oct 29, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 551-555
• Book Title: 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA)
• ISBN: 9781665429788
• DOI: https://doi.org/10.1109/icaacca51523.2021.9465179
• Public URL: https://nottingham-repository.worktribe.com/output/6544049
• Publisher URL: https://ieeexplore.ieee.org/document/9465179
• Additional Information: © 2021 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.