Maestre, J.M., Trodden, P.A. orcid.org/0000-0002-8787-7432 and Ishii, H. (2019) A distributed model predictive control scheme with robustness against noncompliant controllers. In: 2018 IEEE Conference on Decision and Control (CDC). 57th IEEE Conference on Decision and Control (CDC 2018), 17-19 Dec 2018, Florida, USA. IEEE ISBN 978-1-5386-1395-5
Abstract
A tube-based distributed model predictive control (DMPC) scheme is proposed for dynamically coupled linear systems. The control scheme is designed to guarantee local performance even when neighboring controllers are not complying with the requirements of the algorithm (e.g., they are malicious or faulty). The resulting conservativeness is minimized, for controllers aim to minimize their state and input constraint sets to reduce mutual disturbances. Also, sufficient conditions for feasibility and exponential stability are given. Finally, these ideas are illustrated and assessed with respect to other robust DMPC via a simulated example.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Optimization; Standards; Power system stability; Predictive control; Uncertainty; Nickel; Economic indicators |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Mar 2019 13:27 |
Last Modified: | 21 Jan 2020 01:39 |
Published Version: | https://doi.org/10.1109/CDC.2018.8619079 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/CDC.2018.8619079 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143399 |