[Poster Presentation]Long-Horizon Direct Model Predictive Control with Reduced Computational Complexity

IMPORTANT DATES

Digest Submission Opens: Oct. 30, 2022
Digest Submission Deadline: Mar. 20, 2023
Acceptance Notification: Apr. 28, 2023
Full Paper Submission Deadline: May. 20, 2023

Long-Horizon Direct Model Predictive Control with Reduced Computational Complexity

ID：112 View Protection：PUBLIC Updated Time：2023-06-14 14:50:17 Hits：778 Poster Presentation

Start Time：2023-06-19 10:15 （Asia/Shanghai）

Duration：0min

Session：[E] Poster Session » [E3] Poster Session 3

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Abstract

The paper proposes a strategy that reduces the computational burden of enumeration-based direct model predictive control (MPC) with long prediction intervals. This is achieved by combining a move blocking principle with an educated restriction of the set of candidate solutions. To demonstrate the effectiveness of the proposed algorithm, a two-level converter connected to the grid via an LCL filter serves as a case study.

Keywords

Model predictive control (MPC);grid-connected converter;integer programming;LCL filter;optimal control

Speaker

Mattia Rossi

Postdoctoral researc Tampere University

Mattia Rossi received the B.Sc. and M.Sc. degrees in automation and control engineering from Politecnico di Milano, Milan, Italy, in 2013 and 2015, respectively. In 2021, he received the Ph.D. in electrical engineering from Politecnico di Milano, Milan, Italy, in collaboration with Tampere University, Tampere, Finland. In 2015, he was with the ABB Ltd, Turgi, Switzerland, where he worked on the design of motor control strategies for medium-voltage drives. In 2019, he was a visiting Ph.D. student in the Chair of Electrical Drive Systems and Power Electronics, Technische Universitat Munchen, Munich, Germany. In 2021, he co-founded the startup ePEBBs Srl, located in Italy. Since 2022, he is with the Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland, where he is currently a Postdoctoral Research Fellow. His main research activities cover the formulation and embedded implementation of model predictive control algorithms for power electronic-based systems, aiming to improve power conversion efficiency and system components reliability.

Petros Karamanakos

Associate professor Tampere University

Petros Karamanakos is an Associate Professor at the Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland. Dr. Karamanakos received the Diploma and the Ph.D. degrees in electrical and computer engineering from the National Technical University of Athens (NTUA), Athens, Greece, in 2007, and 2013, respectively. Prior to joining Tampere University, he was with the ABB Corporate Research Center, Baden-Dättwil, Switzerland, and the Chair of Electrical Drive Systems and Power Electronics, Technische Universität München, Munich, Germany. His main research interests lie at the intersection of optimal control and modulation, mathematical programming and power electronics, including model predictive control for utility-scale power converters and ac variable speed drives.

Arto Sankala

Lead control enginee Danfoss Drives

Arto Sankala is a Lead Control Engineer at Danfoss Drives, Tampere, Finland. He received the B.Sc., M.Sc. and Ph.D. degrees in electrical engineering from Lappeenranta University of Technology (LUT), Finland. His doctoral research covered the control of Medium Voltage Drives in cooperation with Vacon (now Danfoss). During 2009, he was an Assistant Scientist with the Department of Electrical Engineering, LUT. His current research interests include control concepts for electrical drives, field-programmable gate arrays (FPGAs) and switching-mode audio amplifiers.

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