Multi-objective sequential model predictive control for high-power railway induction motor application

Araya, Baldomero; Rivera, Marco; Restrepo, Carlos; Wheeler, Patrick; Zerdali, Emrah

Abstract

Model Predictive Control (MPC) has demonstrated its effectiveness in several industrial applications, but it grapples with weighting factor (WF) tuning challenges. This paper presents a comparative analysis between conventional MPC and Sequential MPC (SMPC) for an induction motor driven by a three-level neutral point clamped (3L-NPC) inverter. Unlike classical MPC, the SMPC technique does not incorporate a global cost function. Instead, it employs a cascade of simple cost functions, thereby eliminating WFs. Simulation studies were conducted using parameters of a high-power motor for railway applications, incorporating up to four control objectives. The comparison metric employed is the steady-state total harmonic distortion index, with SMPC prevailing in the majority of scenarios. This outcome signifies the potential of achieving commendable motor performance while circumventing the arduous WF tuning process. Additionally, a parameter variation test was conducted, demonstrating the control’s robustness against such testing conditions.

Citation

Araya, B., Rivera, M., Restrepo, C., Wheeler, P., & Zerdali, E. (2024, June). Multi-objective sequential model predictive control for high-power railway induction motor application. Presented at 13th International Conference on Power Electronics, Machines and Drives (PEMD 2024), Nottingham, England

• Presentation Conference Type: Edited Proceedings
• Conference Name: 13th International Conference on Power Electronics, Machines and Drives (PEMD 2024)
• Start Date: Jun 10, 2024
• End Date: Jun 13, 2024
• Acceptance Date: Jun 14, 2024
• Online Publication Date: Jun 14, 2024
• Publication Date: 2024-06
• Deposit Date: Nov 19, 2024
• Electronic ISSN: 2732-4494
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• Volume: 2024
• Issue: 3
• Pages: 613-619
• Series ISSN: 2732-4494
• Book Title: 13th International Conference on Power Electronics, Machines and Drives (PEMD 2024)
• DOI: https://doi.org/10.1049/icp.2024.2216
• Public URL: https://nottingham-repository.worktribe.com/output/40002471
• Publisher URL: https://digital-library.theiet.org/doi/10.1049/icp.2024.2216