A hierarchical multiple model adaptive control of discrete-time stochastic systems for sensor and actuator uncertainties

Watanabe, K; Tzafestas, SG

dc.contributor.author	Watanabe, K	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T01:07:46Z
dc.date.available	2014-03-01T01:07:46Z
dc.date.issued	1990	en
dc.identifier.issn	0005-1098	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/10153
dc.subject	Decentralized control	en
dc.subject	Failure detection	en
dc.subject	Hierarchical decision making	en
dc.subject	Kalman filters	en
dc.subject	Parameter estimation	en
dc.subject.classification	Automation & Control Systems	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Actuators	en
dc.subject.other	Computer Programming--Algorithms	en
dc.subject.other	Control Systems, Discrete Time	en
dc.subject.other	Control Systems, Stochastic	en
dc.subject.other	Probability	en
dc.subject.other	Decentralized Control	en
dc.subject.other	Failure Detection and Identification	en
dc.subject.other	Control Systems, Adaptive	en
dc.title	A hierarchical multiple model adaptive control of discrete-time stochastic systems for sensor and actuator uncertainties	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/0005-1098(90)90004-2	en
heal.identifier.secondary	http://dx.doi.org/10.1016/0005-1098(90)90004-2	en
heal.language	English	en
heal.publicationDate	1990	en
heal.abstract	A hierarchical multiple model adaptive control (MMAC) is described for discrete-time stochastic systems with unknown sensor and actuator parameters, where the decentralized structure consists of a central processor and of m local processors which do not communicate between each other. A major assumption in this study is that the central and any local stations have different knowledge of the hypotheses on the unknown parameters. This leads to a flexible design algorithm for passively adaptive control strategies. Furthermore, the coordinator algorithm in evaluating the global a posteriori probability is relatively simple to implement. The result is applied to the design problem of an instrument failure detection and identification (FDI) system.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Automatica	en
dc.identifier.doi	10.1016/0005-1098(90)90004-2	en
dc.identifier.isi	ISI:A1990DZ72500004	en
dc.identifier.volume	26	en
dc.identifier.issue	5	en
dc.identifier.spage	875	en
dc.identifier.epage	886	en