Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach

Franco, Roberto; Ríos, Héctor; Efimov, Denis; Perruquetti, Wilfrid

doi:10.1109/cdc.2018.8619104

Cited by 3 publications

(1 citation statement)

References 20 publications

(29 reference statements)

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“…Some simulation results illustrate the performance of the proposed nonlinear adaptive sliding‐mode observer. The preliminary investigations in this direction can be found in Reference 20 for the case of finite‐time convergence, without proofs and without analysis of the measurement noise influence.…”

Section: Introductionmentioning

confidence: 99%

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

Franco

Ríos

Efimov

et al. 2020

Intl J Robust & Nonlinear

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This paper deals with the problem of adaptive estimation, i.e. 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.

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Section: Introductionmentioning

confidence: 99%

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

Franco

Ríos

Efimov

et al. 2020

Intl J Robust & Nonlinear

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Design of a joint adaptive observer for a class of affine nonlinear sampled‐output system with unknown states and parameters

Zhuang

Wang

Ali

et al. 2021

Adaptive Control & Signal

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Summary In this article, a joint adaptive observer design method is proposed for a class of affine nonlinear systems subject to sampled output data measurements. The considered class of system contains nonlinear terms which depend on unknown parameters. The considered unknown parameters enter the system in both the output and the system states equations which render the design of the sampled data observer for the affine nonlinear system more difficult to conceive. To solve this problem, and based on a new method of decoupling parameter estimation and state observation, a new online output sampling joint adaptive observer is proposed in this article, which can simultaneously guarantees the exponential convergence of the estimation of unknown state and parameter. The structure of the proposed observer has been extended to the case of sampled and delayed data measurements. To illustrate the performance of the proposed observer, a comparison is made with another observer with an output predictor on a satellite navigation system. And the observer proposed in this article is applied to the model‐free control of ultra‐local models.

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Adaptive parameter estimation for infinite-dimensional LTI systems with finite-time convergence

Kapetina

Pisano

Rapaić

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach

Cited by 3 publications

References 20 publications

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

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

Design of a joint adaptive observer for a class of affine nonlinear sampled‐output system with unknown states and parameters

Adaptive parameter estimation for infinite-dimensional LTI systems with finite-time convergence

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