Dughman, S.S. and Rossiter, J.A. orcid.org/0000-0002-1336-0633 (2017) The feasibility of parametric approaches to predictive control when using far future feed forward information. In: 2017 13th IEEE International Conference on Control & Automation (ICCA). 13th IEEE International Conference on Control and Automation, 03/07/2017 - 06/07/2017, Ohrid, Macedonia. Institute of Electrical and Electronics Engineers
Abstract
This paper considers the tractability of parametric solvers for predictive control based optimisations, when future target information is incorporated. it is shown that the inclusion of future target information can significantly increase the implied parametric dimension to an extent that is undesirable and likely to lead to intractable problems. The paper then proposes some alternative methods for incorporating the desired target information, while minimising he implied growth in the parametric dimensions, at some possibly small cost to optimality.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper subsequently published in 2017 13th IEEE International Conference on Control & Automation (ICCA). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Parametric predictive control; advance knowledge; computational efficiency |
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: | 23 Mar 2017 10:06 |
Last Modified: | 03 Nov 2017 01:55 |
Published Version: | https://doi.org/10.1109/ICCA.2017.8003215 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/ICCA.2017.8003215 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:113755 |