An interative learning control scheme using the weighted least-squares method

Watanabe, K; Fukuda, T; Tzafestas, SG

dc.contributor.author	Watanabe, K	en
dc.contributor.author	Fukuda, T	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T01:08:14Z
dc.date.available	2014-03-01T01:08:14Z
dc.date.issued	1991	en
dc.identifier.issn	0921-0296	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/10365
dc.subject	impulse response model	en
dc.subject	Iterative learning control	en
dc.subject	Kalman filter	en
dc.subject	robot manipulator	en
dc.subject	sparse system structure	en
dc.subject	weighted least-squares method	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Robotics	en
dc.title	An interative learning control scheme using the weighted least-squares method	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/BF00303227	en
heal.identifier.secondary	http://dx.doi.org/10.1007/BF00303227	en
heal.language	English	en
heal.publicationDate	1991	en
heal.abstract	An iterative learning control scheme is described for linear discrete-time systems. A weighted least-squares criterion of learning error is optimized to obtain a unique control gain for a case when the number of sampling is relatively small. It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter. By paying attention to the sparse system structure for the system's impulse response model, we further derive a suboptimal iterative learning control for a practical case when the number of sampling is large. © 1991 Kluwer Academic Publishers.	en
heal.publisher	Kluwer Academic Publishers	en
heal.journalName	Journal of Intelligent & Robotic Systems	en
dc.identifier.doi	10.1007/BF00303227	en
dc.identifier.isi	ISI:A1991GE15300004	en
dc.identifier.volume	4	en
dc.identifier.issue	3	en
dc.identifier.spage	267	en
dc.identifier.epage	284	en