A Current Sensorless Computationally Efficient Model Predictive Control for Matrix Converters

Sarajian, Ali; Guan, Quanxue; Wheeler, Patrick; Khaburi, Davood Arab; Kennel, Ralph; Rodriquez, Jose

Abstract

Model Predictive Control (MPC) is becoming more popular than ever as an alternative to conventional modulations such as Space Vector Modulation methods to control matrix converters (MCs). However, the implementation of MPC is computationally expensive, because control objectives are required to evaluate all admissible switching states of the converter. Additionally, a large number of sensors to measure the 3-phase load currents, source currents, source voltages, and input voltages of MCs increases the overall cost. To sort this out, an efficient MPC is proposed for MCs to enable fast computation and low cost. This approach eliminates the calculations of future load currents and source currents for all possible switching states, requiring only two predictions for the calculation of output voltage and input current references. Further, it removes all current sensors by employing a Luenberger observer. A simulation study has demonstrated that the proposed method can reduce the computation overhead and hardware cost dramatically, leading to high-frequency operation and good converter performance.

Citation

Sarajian, A., Guan, Q., Wheeler, P., Khaburi, D. A., Kennel, R., & Rodriquez, J. (2022, October). A Current Sensorless Computationally Efficient Model Predictive Control for Matrix Converters. Presented at IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium

• Presentation Conference Type: Edited Proceedings
• Conference Name: IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
• Start Date: Oct 17, 2022
• End Date: Oct 20, 2022
• Acceptance Date: Jul 10, 2022
• Online Publication Date: Oct 17, 2022
• Publication Date: Oct 17, 2022
• Deposit Date: Sep 5, 2022
• Publicly Available Date: Oct 17, 2022
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1052-1057
• Book Title: IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
• ISBN: 9781665480260
• DOI: https://doi.org/10.1109/iecon49645.2022.9969053
• Public URL: https://nottingham-repository.worktribe.com/output/10908813
• Publisher URL: https://ieeexplore.ieee.org/document/9969053
• Related Public URLs: https://iecon2022.org/