Zhang, Z., Xie, L., Lu, S. et al. (2 more authors) (2022) A low-cost pole-placement MPC algorithm for controlling complex dynamic systems. Journal of Process Control, 111. pp. 106-116. ISSN 0959-1524
Abstract
Due to the ability to handle constraints systematically and predict system evolution with models, model predictive control (MPC) methods have been widely studied and implemented in many industries. At the same time, low-cost MPC has received widespread attention due to its simple principle and easy implementation. This paper proposes a new low-cost MPC method and uses this method to put forward new insights on the performance improvement of complex dynamic system control. Firstly, using the concept of preprocessing, a novel MPC prediction structure is proposed under the independent model mode. Then through rigorous proofs, the properties of the proposed MPC algorithm are analyzed. Finally, through the study of three industrial cases, the proposal’s deployment procedure and efficacy are illustrated in detail. Compared with other low-cost predictive control algorithms, the effectiveness of the method has been presented.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier Ltd. This is an author produced version of a paper subsequently published in Journal of Process Control. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Low-cost MPC; pole-placement; complex dynamic systems; prediction structure design |
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: | 15 Feb 2022 11:26 |
Last Modified: | 22 Feb 2023 01:13 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.jprocont.2022.02.001 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183319 |
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