dc.contributor.author | Bradford, Eric | |
dc.contributor.author | Reble, Marcus | |
dc.contributor.author | Imsland, Lars Struen | |
dc.date.accessioned | 2020-03-25T14:09:56Z | |
dc.date.available | 2020-03-25T14:09:56Z | |
dc.date.created | 2020-03-24T02:49:50Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | European Control Conference (ECC). 2019, 18 | en_US |
dc.identifier.isbn | 978-3-907144-00-8 | |
dc.identifier.uri | https://hdl.handle.net/11250/2648634 | |
dc.description.abstract | Nonlinear model predictive control (NMPC) is one of the few methods that can handle multivariate nonlinear control problems while accounting for process constraints. Many dynamic models are however affected by significant stochastic uncertainties that can lead to closed-loop performance problems and infeasibility issues. In this paper we propose a novel stochastic NMPC (SNMPC) algorithm to optimize a probabilistic objective while adhering chance constraints for feasibility in which only noisy measurements are observed at each sampling time. The system predictions are assumed to be both affected by parametric and additive stochastic uncertainties. In particular, we use polynomial chaos expansions (PCE) to expand the random variables of the uncertainties. These are updated using a PCE nonlinear state estimator and exploited in the SNMPC formulation. The SNMPC scheme was verified on a complex polymerization semi-batch reactor case study. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | 2019 18th European Control Conference (ECC) | |
dc.subject | Kybernetikk | en_US |
dc.subject | Cybernetics | en_US |
dc.title | Output feedback stochastic nonlinear model predictive control of a polymerization batch process | en_US |
dc.type | Chapter | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | acceptedVersion | en_US |
dc.subject.nsi | VDP::Elektrotekniske fag: 540 | en_US |
dc.subject.nsi | VDP::Electro-technical sciences: 540 | en_US |
dc.source.pagenumber | 3144-3151 | en_US |
dc.identifier.doi | 10.23919/ECC.2019.8795684 | |
dc.identifier.cristin | 1803108 | |
dc.relation.project | EC/H2020/675215 | en_US |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |