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.
|Journal||International Journal of Robust and Nonlinear Control|
|State||Accepted/In press - 2020|
Bibliographical noteFunding Information:
R. Franco and H. Ríos thank the financial support from CONACYT CVU 772057, and Cátedras CONACYT CVU 270504 project 922, respectively, and from TECNM project 8417.20‐P.
Consejo Nacional de Ciencia y Tecnología, CVU 772057; Consejo Nacional de Ciencia y Tecnología, Cítedras CONACYT CVU 270504 project 922; Tecnológico Nacional de México, project 8417.20‐P; Government of Russian Federation, Grant 08‐08; Ministry of Science and Higher Education of Russian Federation, passport of goszadanie, no. 2019‐0898 Funding information
D. Efimov thanks the financial support by the Government of Russia Federation (Grant 08‐08) and by the Ministry of Science and Higher Education of Russian Federation, passport of goszadanie no. 2019‐0898.
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- adaptive observer
- nonlinear systems