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Robust adaptive neural control for a class of uncertain non-linear time-delay systems with unknown dead-zone non-linearity

机译:一类不确定死区非线性的不确定非线性时滞系统的鲁棒自适应神经控制

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摘要

A robust adaptive neural network controller is proposed for a class of uncertain non-linear time-delay systems in strict feedback form with both completely unknown control gains and unknown non-symmetric dead-zone non-linearity based on backstepping design. The proposed design approach does not require a priori knowledge of the signs of the unknown control gains. The unknown time delays are compensated for constructing appropriate Lyapunov??Krasovskii functionals. By utilising integral Lyapunov design and sliding-mode control strategy, the controller singularity problem and the effect of dead-zone input non-linearity are avoided perfectly. From Lyapunov stability theorem, it is proved that the proposed design approach is able to guarantee semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system, and the tracking error of the system is proven to be converged to a small neighbourhood of the origin. The simulation results demonstrate the effectiveness of the proposed approach.
机译:基于反推设计,针对一类具有严格反馈形式的不确定非线性不确定时滞系统,提出了一种鲁棒的自适应神经网络控制器,该系统具有严格未知的控制增益和未知的非对称死区非线性。提出的设计方法不需要先验知识的未知控制增益的迹象。为了构造适当的Lyapunov ?? Krasovskii功能,可以补偿未知的时间延迟。通过使用整体Lyapunov设计和滑模控制策略,可以完美避免控制器奇异性问题和死区输入非线性的影响。根据Lyapunov稳定性定理,证明了所提出的设计方法能够保证闭环系统中所有信号的半全局一致最终有界性,并且证明该系统的跟踪误差收敛于较小的邻域。原产地。仿真结果证明了该方法的有效性。

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  • 来源
    《Control Theory & Applications, IET》 |2011年第15期|p.1782-1795|共14页
  • 作者

    Wang J.; Hu J.;

  • 作者单位

    Engineering College, Air Force Engineering University, Xi'an 710038, Shaanxi, People's Republic of China;

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  • 正文语种 eng
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