An iterative learning scheme-based fault estimation observer is designed for a class of nonlinear systems with randomly changed trial length. This is achieved by presenting a state observer for monitoring the system state and an iterative learning law for fault estimation in the presence of imprecise system model. An average factor is defined to deal with the lack and redundancy in tracking information caused by random trial length. Via the convergence analysis, sufficient design conditions are developed for estimation of fault signal. The observer gains and iterative learning law indexes are computed by solving the proposed conditions under - constraints. Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.
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Publication date: Available online 4 January 2018 Source: European Journal of Radiology Author(s): Peiyao Zhang, Jing Wang, Qin Xu, Zhen...
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Publication date: March 2017 Source: Free Radical Biology and Medicine, Volume 104 from #AlexandrosSfakianakis via Alexandros G.Sfak...
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Dtsch med Wochenschr DOI: 10.1055/s-0043-100054 Hintergrund und Fragestellung Ein etablierter Weg, die optimale Behandlung von Tumorpatien...
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Background Hyperthyroidism is associated with increased thrombotic risk. As contact system activation through formation of neutrophil extrac...
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Deepak Thapa, Vanita Ahuja, Deepanshu Dhiman Indian Journal of Anaesthesia 2017 61(12):1012-1014 from #AlexandrosSfakianakis via Alexa...
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Abstract Limited memory size is considered as a major bottleneck in data centers for intelligent urban computing. It is shown that there e...
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ecancer is supporting #BowelCancerAwarenessMonth https://t.co/opXxCAAxzE from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inore...
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Linked Article: Maintz et al. Br J Dermatol 2017; 176:481–487 . from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader h...
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Angelina Jolie’s openness about her diagnosis has undoubtedly raised public awareness of the importance of inherited cancers. Although Desai...
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