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Learning of model-plant mismatch map via neural network modeling and its application to offset-free model predictive control
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Son, Sang Hwan | - |
dc.contributor.author | Kim, Jong Woo | - |
dc.contributor.author | Oh, Tae Hoon | - |
dc.contributor.author | Jeong, Dong Hwi | - |
dc.contributor.author | Lee, Jong Min | - |
dc.date.accessioned | 2022-08-22T09:03:58Z | - |
dc.date.available | 2022-08-22T09:03:58Z | - |
dc.date.created | 2022-06-27 | - |
dc.date.issued | 2022-07 | - |
dc.identifier.citation | Journal of Process Control, Vol.115, pp.112-122 | - |
dc.identifier.issn | 0959-1524 | - |
dc.identifier.uri | https://hdl.handle.net/10371/184266 | - |
dc.description.abstract | We propose an improved offset-free model predictive control (MPC) framework, which learns and utilizes the intrinsic model-plant mismatch map, to effectively exploit the advantages of model based and data-driven control strategies and overcome the limitation of each approach. In this study, the model-plant mismatch map on steady-state manifold is approximated via artificial neural network (ANN) modeling based on steady-state data from the process. Though the learned model plant mismatch map can provide the information at the equilibrium point (i.e., setpoint), it cannot provide model-plant mismatch information during transient state. To handle this, we additionally apply a supplementary disturbance variable which is updated from a revised disturbance estimator considering the disturbance value obtained from the learned model-plant mismatch map. Then, the learned and supplementary disturbance variables are applied to the target problem and finite-horizon optimal control problem of the offset-free MPC framework. By this, the control system can utilize both the learned model-plant mismatch information and the stabilizing property of the nominal disturbance estimator. The closed-loop simulation results demonstrate that the proposed offset-free MPC scheme utilizing the model-plant mismatch map learned via ANN modeling efficiently improves the closed-loop reference tracking performance of the control system. Additionally, the zero-offset tracking condition of the developed framework is mathematically examined. (C) 2022 Elsevier Ltd. All rights reserved. | - |
dc.language | 영어 | - |
dc.publisher | Butterworth Scientific Ltd. | - |
dc.title | Learning of model-plant mismatch map via neural network modeling and its application to offset-free model predictive control | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.jprocont.2022.04.014 | - |
dc.citation.journaltitle | Journal of Process Control | - |
dc.identifier.wosid | 000809739600011 | - |
dc.identifier.scopusid | 2-s2.0-85130233945 | - |
dc.citation.endpage | 122 | - |
dc.citation.startpage | 112 | - |
dc.citation.volume | 115 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Lee, Jong Min | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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