dc.contributor.author | Bradford, Eric | |
dc.contributor.author | Imsland, Lars Struen | |
dc.contributor.author | Zhang, Dongda | |
dc.contributor.author | Chanona del Rio, Ehecatl Antonio | |
dc.date.accessioned | 2020-06-02T07:40:45Z | |
dc.date.available | 2020-06-02T07:40:45Z | |
dc.date.created | 2020-05-23T14:52:44Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 0098-1354 | |
dc.identifier.uri | https://hdl.handle.net/11250/2656081 | |
dc.description.abstract | Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) present a powerful tool to identify the required plant model and quantify the residual uncertainty of the plant-model mismatch. It is crucial to consider this uncertainty, since it may lead to worse control performance and constraint violations. In this paper we propose a new method to design a GP-based NMPC algorithm for finite horizon control problems. The method generates Monte Carlo samples of the GP offline for constraint tightening using back-offs. The tightened constraints then guarantee the satisfaction of chance constraints online. Advantages of our proposed approach over existing methods include fast online evaluation, consideration of closed-loop behaviour, and the possibility to alleviate conservativeness by considering both online learning and state dependency of the uncertainty. The algorithm is verified on a challenging semi-batch bioprocess case study. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.uri | https://doi.org/10.1016/j.compchemeng.2020.106844 | |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Stochastic data-driven model predictive control using gaussian processes | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.volume | 139 | en_US |
dc.source.journal | Computers and Chemical Engineering | en_US |
dc.identifier.doi | 10.1016/j.compchemeng.2020.106844 | |
dc.identifier.cristin | 1812238 | |
dc.relation.project | EC/H2020/675215 | en_US |
dc.description.localcode | DOI:10.1016/j.compchemeng.2020.106844. j.compchemeng.2020.106844 0098-1354/© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/ | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |