Guest Editorial Model Predictive Control in Energy Conversion Systems

Tomislav Dragicevic, Alessandra Parisio, Jose Rodriguez, Colin Jones, Daniel Quevedo, Luca Ferrarini, Matthias Preindl, Qobad Shafiee, Thomas Morstyn

Research output: Contribution to journalEditorialResearchpeer-review

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Abstract

The papers in this special section focus on model predictive control (MPC) in energy conversion systems. MPC refers to a broad range of control strategies that make explicit use of a model of the system/device to be controlled optimally. In order to obtain the optimal control signal (or sequence of control signals), MPC optimizes a certain cost function at regular intervals.

Due to its unique capabilities to deal with constraints on actuators and system states as well as its theoretical basis, MPC has been widely received and successfully used for many decades, mostly for control of slow industrial plants. However, with continuous advances of control theory and increasing computational capabilities of modern microprocessors, this control strategy has recently became a technically feasible solution for control of energy conversion systems that operate at much faster times scales.
Original languageEnglish
Article number9439212
JournalIEEE Transactions on Energy Conversion
Volume36
Issue number2
Pages (from-to)1311-1312
ISSN0885-8969
DOIs
Publication statusPublished - Jun 2021

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