Adaptive estimation for uncertain nonlinear systems with measurement noise: A sliding-mode observer approach

Roberto Franco, Héctor Ríos, Denis Efimov, Wilfrid Perruquetti

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

This article deals with the problem of adaptive estimation, that is, the simultaneous estimation of the state and time-varying parameters, in the presence of measurement noise and state disturbances, for a class of uncertain nonlinear systems. An adaptive observer is proposed based on a nonlinear time-varying parameter identification algorithm and a sliding-mode observer. The nonlinear time-varying parameter identification algorithm provides a fixed-time rate of convergence, to a neighborhood of the origin, while the sliding-mode observer ensures ultimate boundedness for the state estimation error attenuating the effects of the external disturbances. Linear matrix inequalities are provided for the synthesis of the adaptive observer while the convergence proofs are given based on the Lyapunov and input-to-state stability theory. Finally, some simulation results show the feasibility of the proposed approach.

Idioma originalInglés
Páginas (desde-hasta)3809-3826
Número de páginas18
PublicaciónInternational Journal of Robust and Nonlinear Control
Volumen31
N.º9
DOI
EstadoPublicada - jun 2021

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© 2020 John Wiley & Sons Ltd

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