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Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

Author

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  • Jidong Wang
  • Lengxue Zhu
  • Xiaoping Si

Abstract

In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

Suggested Citation

  • Jidong Wang & Lengxue Zhu & Xiaoping Si, 2017. "Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs," Complexity, Hindawi, vol. 2017, pages 1-8, March.
    • Handle: RePEc:hin:complx:5628932
    DOI: 10.1155/2017/5628932
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    References listed on IDEAS

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    1. Xia Wang & Jun Zhao, 2015. "Switched adaptive tracking control of robot manipulators with friction and changing loads," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(6), pages 955-965, April.
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    Cited by:

    1. Cui, Di & Zou, Wencheng & Guo, Jian & Xiang, Zhengrong, 2022. "Neural network-based adaptive finite-time tracking control of switched nonlinear systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    2. Xu, Ning & Zhao, Xudong & Zong, Guangdeng & Wang, Yuanqing, 2021. "Adaptive control design for uncertain switched nonstrict-feedback nonlinear systems to achieve asymptotic tracking performance," Applied Mathematics and Computation, Elsevier, vol. 408(C).

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