Adaptive neural control for strict-feedback stochastic nonlinear systems with time-delay

Nonlinear Dynamics (Impact Factor: 2.85). 02/2012; 77(1):267-274. DOI: 10.1016/j.neucom.2011.08.020
Source: DBLP


The problem of robust stabilization is investigated for strict-feedback stochastic nonlinear time-delay systems via adaptive neural network approach. Neural networks are used to model the unknown packaged functions, then the adaptive neural control law is constructed by a novel Lyapunov–Krasovskii functional and backstepping. It is shown that all the variables in the closed-loop system are semi-globally stochastic bounded, and the state variables converge into a small neighborhood in the sense of probability.

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    • "Adaptive control design remains open for the important class of switched nonlinear systems that exhibit unknown nonlinearities in subsystem models using an approximator tool. Typically, the tool uses either neural networks (NNs) or fuzzy systems to parameterize the nonlinear systems with unknown nonlinearities [9] [10] [11] [12] [13] [14] [15]. Some results have also been reported for switched nonlinear systems using adaptive neural control method. "
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    ABSTRACT: This paper investigates the adaptive finite-time stabilization of a class of switched nonlinear systems with unknown nonlinear terms using neural networks. Under the assumption that all the growth conditions for the unknown nonlinear perturbation functions are partially known, the common finite-time controller and adaptive law are constructed by extending the adding-a-power-integrator technique and using the backstepping methodology. The unknown parts of the growth conditions are modeled by the neural networks and the known parts are exploited for the controller design. The bounds of neural network approximation errors are assumed to be unknown and are estimated online. It is shown that the state of the closed-loop system is finite-time stable and the parameter estimations are bounded under arbitrary switching. A simulation example is provided to show the effectiveness of the proposed method.
    Neurocomputing 05/2015; 155:177-185. DOI:10.1016/j.neucom.2014.12.033 · 2.08 Impact Factor
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    • "Adaptive controller has the ability to adjust of control parameters without the help of human intelligence. It can tune complex systems better by combining nonlinear controlling methods and intelligent control technology [22] [23]. The results show that adaptive control has the advantage to solve effectively the "
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    Neurocomputing 07/2014; 135:79–85. DOI:10.1016/j.neucom.2013.03.065 · 2.08 Impact Factor
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    ABSTRACT: Using the technique of neural network parameterization and the backstepping method, a novel adaptive neural network control scheme is proposed for a class of stochastic strict-feedback nonlinear systems. Compared with the existing literature, the proposed approach contains only one adaptive parameter that needs to be updated online. By Lyapunov method, it is shown that all signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean square or the sense of four-moment. Simulation results are given to illustrate the effectiveness of the proposed control scheme.
    Control Conference (CCC), 2012 31st Chinese; 01/2012
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