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Title
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Guest Editorial Model Predictive Control in Energy Conversion Systems
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Type
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JournalPaper
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Keywords
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Special issues and sections , Predictive control , Predictive models , Microgrids , Reluctance machines , Voltage control , Torque control , Phase modulation , Integrated circuit modeling
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Abstract
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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.
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Researchers
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Thomas Morstyn (Not In First Six Researchers), Matthias Preindl (Not In First Six Researchers), Luca Ferrarini (Not In First Six Researchers), Daniel E. Quevedo (Fifth Researcher), Colin N. Jones (Fourth Researcher), Jose Rodríguez (Third Researcher), Alessandra Parisio (Second Researcher), Qobad Shafiee (Not In First Six Researchers), Tomislav Dragicevic (First Researcher)
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