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Adaptive neural network tracking design for a class of uncertain nonlinear discrete-time systems with dead-zone

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Abstract

In this paper, the stability and control issues of a class of uncertain nonlinear discrete-time systems in the strict feedback form are investigated. The dead-zone input in the systems, whose property is non-symmetric and discretized, is investigated. The unknown functions in the systems are approximated by using the radial basis function neural networks (RBFNNs). Backstepping design procedure is employed in the controller and the adaptation laws design. Lyapunov analysis method is utilized to prove the stability of the closed-loop system. A simulation example is given to illustrate the effectiveness of the proposed approach.

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Authors and Affiliations

  1. College of Science, Liaoning University of Technology, Jinzhou, 121001, China

    YanJun Liu, Lei Liu & ShaoCheng Tong

Authors
  1. YanJun Liu
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  2. Lei Liu
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  3. ShaoCheng Tong
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Correspondence to YanJun Liu.

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Liu, Y., Liu, L. & Tong, S. Adaptive neural network tracking design for a class of uncertain nonlinear discrete-time systems with dead-zone. Sci. China Inf. Sci. 57, 1–12 (2014). https://doi.org/10.1007/s11432-012-4779-0

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  • DOI: https://doi.org/10.1007/s11432-012-4779-0

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