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Yungang Liu

Bio: Yungang Liu is an academic researcher from Shandong University. The author has contributed to research in topics: Control theory & Nonlinear system. The author has an hindex of 11, co-authored 21 publications receiving 412 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, the problem of output-feedback adaptive stabilization control design for nonholonomic chained systems with strong non-linear drifts was investigated, including modelled nonlinear dynamics, unmodelled dynamics, and those modelled but with unknown parameters.
Abstract: This paper investigates the problem of output-feedback adaptive stabilization control design for non-holonomic chained systems with strong non-linear drifts, including modelled non-linear dynamics, unmodelled dynamics, and those modelled but with unknown parameters. An observer and an estimator are introduced for state and parameter estimates, respectively. By using the integrator backstepping approach and based on the observer and parameter estimator, a constructive design procedure for output-feedback adaptive stabilization control is given. It is shown that, under some conditions, the control design ensures the closed-loop system is globally asymptotically stable when there is no non-linear drift in the first subsystem, and semiglobally asymptotically stable, otherwise. An example is given to show the effectiveness of the theory.

73 citations

Journal ArticleDOI
Yungang Liu1
TL;DR: In this article, an output-feedback adaptive stabilizing controller using integrator backstepping and tuning function techniques is proposed to ensure the original system state converges to the origin whereas all the other closed-loop system states are bounded.

58 citations

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TL;DR: In this paper, a robust adaptive output tracking control for a class of high-order nonlinear uncertain systems is studied under the assumption that the lower bounds of the unknown control coefficients are exactly known.

51 citations

Journal ArticleDOI
TL;DR: The problem of global adaptive stabilisation by state-feedback is investigated for a class of high-order non-linear systems with uncertain control coefficients and zero dynamics, and a recursive design procedure is successfully developed to achieve a continuous adaptive stabilising controller.
Abstract: The problem of global adaptive stabilisation by state-feedback is investigated for a class of high-order non-linear systems with uncertain control coefficients and zero dynamics. First, some appropriate unknown parameters are introduced to obtain the updating laws when adapting control design. Then, by the flexible way of combining adding a power integrator with adaptive technique and the idea of changing supply functions, the requirement on the uncertain control coefficients is relaxed, and a recursive design procedure is successfully developed to achieve a continuous adaptive stabilising controller. Finally, an example is provided to illustrate the correctness of the theoretical results.

46 citations

Journal ArticleDOI
TL;DR: In this article, a continuous adaptive state-feedback controller for a class of uncertain high-order nonlinear systems with time delays is proposed, under somewhat necessary restrictions on the system nonlinearities, by the method of adding a power integrator and the related adaptive technique.
Abstract: This paper is concerned with adaptive stabilization for a class of uncertain high-order nonlinear systems with time delays. To the authors' knowledge, there has been no analogous result. Hence during investigation, the conditions on delay effect and the control design framework should be established for the first time. In this paper, under somewhat necessary restrictions on the system nonlinearities, by the method of adding a power integrator and the related adaptive technique, a procedure is developed to design the continuous adaptive state-feedback controller without overparametrization. Moreover, the uniform stability and convergence of the resulting closed-loop system are rigorously proven, with the aid of a suitable Lyapunov-Krasovskii functional. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical result.

40 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper investigates the finite-time stabilization problem for a class of high-order uncertain nonlinear systems and proposes a novel control strategy combining sign function with delicate adaptive technique that can handle serious uncertainty and nonlinear growth rate.

232 citations

Journal ArticleDOI
TL;DR: Under the assumption that the time-varying delays exist in the system output, only one NN is employed to compensate for all unknown nonlinear terms depending on the delayed output, and the NN parameters to be estimated are greatly decreased and the online learning time is dramatically decreased.
Abstract: This paper presents an adaptive output-feedback neural network (NN) control scheme for a class of stochastic nonlinear time-varying delay systems with unknown control directions. To make the controller design feasible, the unknown control coefficients are grouped together and the original system is transformed into a new system using a linear state transformation technique. Then, the Nussbaum function technique is incorporated into the backstepping recursive design technique to solve the problem of unknown control directions. Furthermore, under the assumption that the time-varying delays exist in the system output, only one NN is employed to compensate for all unknown nonlinear terms depending on the delayed output. Moreover, by estimating the maximum of NN parameters instead of the parameters themselves, the NN parameters to be estimated are greatly decreased and the online learning time is also dramatically decreased. It is shown that all the signals of the closed-loop system are bounded in probability. The effectiveness of the proposed scheme is demonstrated by the simulation results.

195 citations

Journal ArticleDOI
TL;DR: A new form of K-filters with time-varying low-gain is introduced in this paper to compensate for unmeasurable/unknown states of stochastic feedforward systems with unknown control coefficients and unknown output function.

177 citations