2017
DOI: 10.1002/acs.2765
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Adaptive finite‐time control for high‐order nonlinear systems with mismatched disturbances

Abstract: In this paper, adaptive finite-time control is addressed for a class of high-order nonlinear systems with mismatched disturbances. An adaptive finite-time controller is designed in which variable gains are adjusted to ensure finite-time stabilization for the closed-loop system. Chattering is reduced by a designed adaptive sliding mode observer which is also used to deal with the mismatched disturbances in finite time. The proposed adaptive finite-time control method avoids calculating derivative repeatedly of … Show more

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Cited by 16 publications
(12 citation statements)
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“…To conclude this section about fully model‐based adaptation, we can cite other recent works, ie, post the latest general survey paper, which can be classified under the model‐based paradigm: for nonlinear models,) for models with time delay,) with parameter‐independent realization controllers, with input/output quantization,) under state constraints,) under inputs and actuator‐bandwidth constraints,) for Markovian jump systems,) for switched systems,) for partial differential equation (PDE)–based models,) for nonminimum/minimum‐phase systems,) to achieve adaptive regulation and disturbance rejection,) multiple‐model and switching adaptive control,) linear quadratic regulator (LQR)–based adaptive control, model predictive control–based adaptive control,) applications of model‐based adaptive control,) for sensor/actuator fault mitigation,) for rapidly time‐varying uncertainties, nonquadratic Lyapunov function–based MRAC, for stochastic systems,) retrospective cost adaptive control, persistent excitation–free/data accumulation–based control or concurrent adaptive control, sliding mode–based adaptive control,) set‐theoretic–based adaptive controller with performance guarantees, sampled data systems, and robust adaptive control …”
Section: Model‐based Adaptive Controlmentioning
confidence: 99%
“…To conclude this section about fully model‐based adaptation, we can cite other recent works, ie, post the latest general survey paper, which can be classified under the model‐based paradigm: for nonlinear models,) for models with time delay,) with parameter‐independent realization controllers, with input/output quantization,) under state constraints,) under inputs and actuator‐bandwidth constraints,) for Markovian jump systems,) for switched systems,) for partial differential equation (PDE)–based models,) for nonminimum/minimum‐phase systems,) to achieve adaptive regulation and disturbance rejection,) multiple‐model and switching adaptive control,) linear quadratic regulator (LQR)–based adaptive control, model predictive control–based adaptive control,) applications of model‐based adaptive control,) for sensor/actuator fault mitigation,) for rapidly time‐varying uncertainties, nonquadratic Lyapunov function–based MRAC, for stochastic systems,) retrospective cost adaptive control, persistent excitation–free/data accumulation–based control or concurrent adaptive control, sliding mode–based adaptive control,) set‐theoretic–based adaptive controller with performance guarantees, sampled data systems, and robust adaptive control …”
Section: Model‐based Adaptive Controlmentioning
confidence: 99%
“…Till now, the FTC technique was widely applied to settle down the adaptive tracking problems of nonlinear systems. [7][8][9][10][11][12] However, a distinguished feature of the aforementioned researches is that the predefined time of the FTC schemes depend on the initial conditions and increase along with the deviation between initial state and equilibrium point. Moreover, not all the initial conditions are available in practical systems.…”
Section: Introductionmentioning
confidence: 99%
“…For the first time, the finite-time Lyapunov stability theory was established to deal with the flutter problems caused by the sliding mode controller Bernstein,1998, 2000). On the base of the theory, the finite-time control problems of deterministic nonlinear systems have been studied by many authors (Hong, 2002;Kamalamiri et al, 2020;Wang et al, 2018;Yang et al, 2017). Subsequently, with the development of the study, the finite-time control for deterministic nonlinear systems has been generalized to stochastic nonlinear control systems and many significant achievements have been obtained (Chen and Jiao, 2010;Khoo et al, 2013;Liu and Zhu, 2020;Song and Zhai, 2017;Zhang et al, 2018).…”
Section: Introductionmentioning
confidence: 99%