An Approach to Auto-Tuning Distributed Model Predictive Control Suboptimality by Using Neural Networks

Simple item page
creativework.keywordsConstraints, Distributed control, Interconnected systems, Linear systems, Predictive control
creativework.publisherElsevier B.V.en
dc.contributor.authorGrancharova A.
dc.contributor.authorGeorgiev G.
dc.contributor.authorOlaru S.
dc.date.accessioned2026-01-20T13:58:04Z
dc.date.accessioned2026-01-20T15:55:10Z
dc.date.available2026-01-20T13:58:04Z
dc.date.available2026-01-20T15:55:10Z
dc.date.issued2025-07-01
dc.description.abstractIn this paper, an approach to automatic tuning of suboptimal distributed MPC (DMPC) of linear interconnected systems with coupled dynamics subject to both state and input constraints is proposed. The purpose is to obtain a desired closed-loop performance without exceeding a limit on the online computational complexity. The approach includes three stages which are performed offline. First, the optimal tuning of the MPC cost function parameters is obtained for different values of the suboptimal DMPC design parameters by adjusting the DMPC closed-loop performance. Then, a neural network is used to approximate the influence of the design parameters on the performance and the computational complexity. As a third stage, the best choice of the design parameters is determined by solving an optimization problem based on the obtained neural network model. The suggested approach would be appropriate for embedded distributed MPC since it will reduce the complexity of the online MPC computations and simplify the software implementation.
dc.identifier.doi10.1016/j.ifacol.2025.09.528
dc.identifier.issn2405-8963
dc.identifier.issn2405-8971
dc.identifier.scopusSCOPUS_ID:105018800588en
dc.identifier.urihttps://rlib.uctm.edu/handle/123456789/1904
dc.language.isoen
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105018800588&origin=inward
dc.titleAn Approach to Auto-Tuning Distributed Model Predictive Control Suboptimality by Using Neural Networks
dc.typeConference Paper
oaire.citation.issue11
oaire.citation.volume59
Files
Collections
Articles