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Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

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  • Shubo Wang
  • Haisheng Yu
  • Xuehui Gao
  • Na Wang

Abstract

This paper proposes an adaptive barrier controller for servomechanisms with friction compensation. A modified LuGre model is used to capture friction dynamics of servomechanisms. This model is incorporated into an augmented neural network (NN) to account for the unknown nonlinearities. Moreover, a barrier Lyapunov function (BLF) is utilized to each step in a backstepping design procedure. Then, a novel adaptive control method is well suggested to ensure that the full-state constraints are within the given boundary. The stability of the closed-loop control system is proved using Lyapunov stability theory. Comparative experiments on a turntable servomechanism confirm the effectiveness of the devised control method.

Suggested Citation

  • Shubo Wang & Haisheng Yu & Xuehui Gao & Na Wang, 2018. "Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation," Complexity, Hindawi, vol. 2018, pages 1-10, July.
    • Handle: RePEc:hin:complx:8925838
    DOI: 10.1155/2018/8925838
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    Cited by:

    1. Jianhua Zhang & Quanmin Zhu & Yang Li & Xueli Wu, 2019. "Homeomorphism Mapping Based Neural Networks for Finite Time Constraint Control of a Class of Nonaffine Pure-Feedback Nonlinear Systems," Complexity, Hindawi, vol. 2019, pages 1-11, May.

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