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Sampled-Data Adaptive Observer For a Class of State Affine Output-Injection Nonlinear Systems

Abstract : The problem of observer design is addressed for output-injection nonlinear systems. A major difficulty with this class of systems is that the state equation involves an output-dependent term that is explicitly dependent on unknown parameters. As the output is only accessible to measurement at sampling times, the output-dependent term turns out to be (almost all time) subject to a double uncertainty, making previous adaptive observers inappropriate. Presently, a new hybrid adaptive observer is designed and shown to be exponentially convergent under ad-hoc conditions.
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Submitted on : Monday, July 13, 2020 - 3:50:06 PM
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T. Folin, Tarek Hamed-Ali, Fouad Giri, Laurent Burlion, Françoise Lamnabhi-Lagarrigue. Sampled-Data Adaptive Observer For a Class of State Affine Output-Injection Nonlinear Systems. IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2016, 61 (2), pp.462-467. ⟨10.1109/TAC.2015.2437522⟩. ⟨hal-01260152⟩

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