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Disturbance-observer-based sampled-data adaptive output feedback control for a class of uncertain nonlinear systems

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  • Wenmin He
  • Jian Guo
  • Zhengrong Xiang

Abstract

In this paper, a sampled-data adaptive output feedback controller is proposed for a class of uncertain nonlinear systems with unmeasured states, unknown dynamics and unknown time-varying external disturbances. To approximate uncertain nonlinear functions, radial basis function neural networks (RBFNNs) are employed. The state observer and the disturbance observer (DO) are constructed to estimate the unmeasured state and the external disturbance, respectively. Then, the sampled-data adaptive output feedback controller and adaptive laws are designed by using the backstepping design technique. The allowable sampling period T is derived to guarantee that all states of the resulting closed-loop system are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to illustrate the effectiveness of the proposed approach.

Suggested Citation

  • Wenmin He & Jian Guo & Zhengrong Xiang, 2019. "Disturbance-observer-based sampled-data adaptive output feedback control for a class of uncertain nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(9), pages 1771-1783, July.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:9:p:1771-1783
    DOI: 10.1080/00207721.2019.1626930
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