Abdullah, M. and Rossiter, J.A. orcid.org/0000-0002-1336-0633 (2018) The effect of model structure on the noise and disturbance sensitivity of Predictive Functional Control. In: Proceedings of the 2018 European Control Conference (ECC). European Control Conference, 12-15 Jun 2018, Limassol, Cyprus. IEEE , pp. 1009-1014. ISBN 9781538653036
Abstract
An Independent Model (IM) structure has become a standard form used in Predictive Functional Control (PFC) for handling uncertainty. Nevertheless, despite its popularity and efficacy, there is a lack of systematic analysis or academic rigour in the literature to justify this preference. This paper seeks to fill this gap by analysing the effectiveness of different prediction models, specifically the IM structure and T-filter, for handling noise and disturbances. The observations are validated via both closed-loop simulation and real-time implementation and show that the sensitivity relationships are system dependent, which in turn emphasises the importance of performing this analysis to ensure a robust PFC implementation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 EUCA. 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. |
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: | 21 Feb 2018 13:25 |
Last Modified: | 29 Jun 2020 09:19 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/ECC.2018.8550374 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127668 |