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Approximation-based disturbance observer approach for adaptive tracking of uncertain pure-feedback nonlinear systems with unmatched disturbances

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  • Hyoung Oh Kim
  • Sung Jin Yoo

Abstract

This paper presents an approximation-based nonlinear disturbance observer (NDO) methodology for adaptive tracking of uncertain pure-feedback nonlinear systems with unmatched external disturbances. Compared with existing control results using NDO for nonlinear systems in lower-triangular form, the major contribution of this study is to develop an NDO-based control framework in the presence of non-affine nonlinearities and disturbances unmatched in the control input. An approximation-based NDO scheme is designed to attenuate the effect of compounded disturbance terms consisting of external disturbances, approximation errors and control coefficient nonlinearities. The function approximation technique using neural networks is employed to estimate the unknown nonlinearities derived from the recursive design procedure. Based on the designed NDO scheme, an adaptive dynamic surface control system is constructed to ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to a neighbourhood of the origin. Simulation examples including a mechanical system are provided to show the effectiveness of the proposed theoretical result.

Suggested Citation

  • Hyoung Oh Kim & Sung Jin Yoo, 2017. "Approximation-based disturbance observer approach for adaptive tracking of uncertain pure-feedback nonlinear systems with unmatched disturbances," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(8), pages 1775-1786, June.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:8:p:1775-1786
    DOI: 10.1080/00207721.2017.1282062
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    References listed on IDEAS

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    1. Tian-Ping Zhang & Qing Zhu & Yue-Quan Yang, 2012. "Adaptive neural control of non-affine pure-feedback non-linear systems with input nonlinearity and perturbed uncertainties," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(4), pages 691-706.
    2. Ben Niu & Tian Qin & Xiaodong Fan, 2016. "Adaptive neural network tracking control for a class of switched stochastic pure-feedback nonlinear systems with backlash-like hysteresis," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(14), pages 3378-3393, October.
    3. Ping Li & Guang-Hong Yang, 2011. "A novel adaptive control approach for nonlinear strict-feedback systems using nonlinearly parameterised fuzzy approximators," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(3), pages 517-527.
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    Cited by:

    1. Hyoung Oh Kim & Sung Jin Yoo, 2018. "Decentralised disturbance-observer-based adaptive tracking in the presence of unmatched nonlinear time-delayed interactions and disturbances," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(1), pages 98-112, January.
    2. Jeong, Dong Min & Yoo, Sung Jin, 2021. "Adaptive event-triggered tracking using nonlinear disturbance observer of arbitrarily switched uncertain nonlinear systems in pure-feedback form," Applied Mathematics and Computation, Elsevier, vol. 407(C).

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