Neural-network-based adaptive tracking control for nonlinear pure-feedback systems subject to periodic disturbance

Renwei Zuo, Maolong Lv*, Yinghui Li, Hongyan Nie

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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This paper presents an adaptive neural control to solve the tracking problem of a class of pure-feedback systems with non-differentiable non-affine functions in the presence of unknown periodically time-varying disturbances. To handle with the design difficulty from non-affine structure of pure-feedback system, a continuous and positive control gain function is constructed to model the periodically disturbed non-affine function as a form that facilitates the control design. As a result, the non-affine function is not necessary to be differentiable with respect to control variables or input. In addition, the bounds of non-affine function are unknown functions, and some appropriate compact sets are introduced to investigate the bounds of non-affine function so as to cope with the difficulty from these unknown bounds. It is proven that the closed-loop control system is semi-globally uniformly ultimately bounded by choosing the appropriate design parameters. Finally, comparative simulations are provided to illustrate the effectiveness of the proposed control scheme.

Original languageEnglish
Number of pages11
JournalInternational Journal of Control
Publication statusPublished - 2021


  • non-affine function
  • periodic disturbance
  • Pure-feedback system
  • robust compensator


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