Event-triggered control for discrete-time systems

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dc.contributor.author Eqtami, A en
dc.contributor.author Dimarogonas, DV en
dc.contributor.author Kyriakopoulos, KJ en
dc.date.accessioned 2014-03-01T02:46:47Z
dc.date.available 2014-03-01T02:46:47Z
dc.date.issued 2010 en
dc.identifier.uri http://hdl.handle.net/123456789/32853
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-77957806211&partnerID=40&md5=1f5d165774a158080907af70da247143 en
dc.relation.uri http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5531089 en
dc.relation.uri http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05531089 en
dc.subject Continuous Time en
dc.subject Discrete Time System en
dc.subject Measurement Error en
dc.subject Model Predictive Control en
dc.subject Input To State Stable en
dc.subject.other Continuous monitoring en
dc.subject.other Continuous time en
dc.subject.other Control laws en
dc.subject.other Discrete time system en
dc.subject.other Event-triggered en
dc.subject.other Input to state stable en
dc.subject.other Model-predictive control approach en
dc.subject.other Digital control systems en
dc.subject.other Measurement errors en
dc.subject.other Model predictive control en
dc.subject.other Potential flow en
dc.subject.other Predictive control systems en
dc.subject.other Discrete time control systems en
dc.title Event-triggered control for discrete-time systems en
heal.type conferenceItem en
heal.identifier.secondary 5531089 en
heal.publicationDate 2010 en
heal.abstract In this paper, event-triggered strategies for control of discrete-time systems are proposed and analyzed. Similarly to the continuous-time case, the plant is assumed input-to-state stable with respect to measurement errors and the control law is updated once a triggering condition involving the norm of a measurement error is violated. The results are also extended to a self-triggered formulation, where the next control updates are decided at the previous ones, thus relaxing the need for continuous monitoring of the measurement error. The overall framework is then used in a novel Model Predictive Control approach. The results are illustrated through simulated examples. © 2010 AACC. en
heal.journalName Proceedings of the 2010 American Control Conference, ACC 2010 en
dc.identifier.spage 4719 en
dc.identifier.epage 4724 en

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