A distributed model predictive control strategy for back-to-back converters

Tarisciotti, Luca, Lo Calzo, Giovanni, Gaeta, Alberto, Zanchetta, Pericle, Valencia, Felipe and Saez, Doris (2016) A distributed model predictive control strategy for back-to-back converters. IEEE Transactions on Industrial Electronics . ISSN 1557-9948 (In Press)

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In recent years Model Predictive Control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel Distributed Model Predictive Control, which is able to achieve the same performance of the classical Model Predictive Control whilst reducing the computational requirements of its implementation. The proposed control approach is tested on a AC/AC converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/772939
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Predictive control, Nonlinear control systems, Back-to-back converters
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Electrical and Electronic Engineering
Identification Number: https://doi.org/10.1109/TIE.2016.2527693
Depositing User: Eprints, Support
Date Deposited: 03 Aug 2016 12:55
Last Modified: 04 May 2020 17:32
URI: https://eprints.nottingham.ac.uk/id/eprint/35680

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