Multiobjective constrained MPC with simultaneous closed-loop identification

Aggelogiannaki, E; Sarimveis, H

dc.contributor.author	Aggelogiannaki, E	en
dc.contributor.author	Sarimveis, H	en
dc.date.accessioned	2014-03-01T01:24:41Z
dc.date.available	2014-03-01T01:24:41Z
dc.date.issued	2006	en
dc.identifier.issn	08906327	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17391
dc.subject	Adaptive control	en
dc.subject	Closed-loop identification	en
dc.subject	Model predictive control	en
dc.subject	Multiobjective optimization	en
dc.subject	Prediction error method	en
dc.subject.other	Adaptive control systems	en
dc.subject.other	Constraint theory	en
dc.subject.other	Dynamics	en
dc.subject.other	Functions	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Closed-loop identification	en
dc.subject.other	Model predictive control	en
dc.subject.other	Multiobjective optimization	en
dc.subject.other	Prediction error method	en
dc.subject.other	Closed loop control systems	en
dc.title	Multiobjective constrained MPC with simultaneous closed-loop identification	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/acs.892	en
heal.identifier.secondary	http://dx.doi.org/10.1002/acs.892	en
heal.publicationDate	2006	en
heal.abstract	Model predictive control (MPC) methodologies are commonly used techniques for constrained control problems. In this paper, the principle of prioritized multiobjective optimization is incorporated in an adaptive MPC framework in order to improve the closed-loop performance in the case of time-varying systems. Instead of weighting the different control goals, the proposed methodology creates a hierarchy according to the importance of each objective and optimizes each one separately. In each optimization step a constraint is added, so that previous in rank objective functions maintain their optimal values. Adaptive capabilities are introduced in the proposed MPC formulation, by considering the persistent excitation requirement as a top priority objective, which is optimized first. The efficiency of the proposed MPC configuration is evaluated through three dynamic processes and the expected advantages are confirmed. Copyright © 2006 John Wiley & Sons, Ltd.	en
heal.journalName	International Journal of Adaptive Control and Signal Processing	en
dc.identifier.doi	10.1002/acs.892	en
dc.identifier.volume	20	en
dc.identifier.issue	4	en
dc.identifier.spage	145	en
dc.identifier.epage	173	en