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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Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182, Butyric Acid from Probiotic Staphyloco...
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Medicine by Alexandros G. Sfakianakis,Αλέξανδρος Γ. Σφακιανάκης A Novel Technique for Endoscopic Repair of Large Anterior Skull Base Defect...
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2016-10-15T06-30-01Z Source: The Southeast Asian Journal of Case Report and Review Sangita Deepak Kamath, Neeraj Jain, Saurabh Pathak, Ba...
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BACKGROUND AND PURPOSE: Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with cl...
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Abstract The development of focused ion beam-scanning electron microscopy (FIB-SEM) techniques has allowed high-resolution 3D imaging of n...
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