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Adaptive Neural Control for Nonaffine Pure-Feedback System Based on Extreme Learning Machine

机译：基于极端学习机的非共源纯反馈系统的自适应神经控制

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摘要

For nonaffine pure-feedback systems, an adaptive neural control method based on extreme learning machine (ELM) is proposed in this paper. Different from the existing methods, this scheme firstly converts the original system into a nonaffine system containing only one unknown term by equivalent transformation, thus avoiding the cumbersome and complex indirect design process of traditional backstepping methods. Secondly, a high-performance finite-time-convergence-differentiator (FD) is designed, through which the system state variables and their derivatives are accurately estimated to ensure the control effect. Thirdly, based on the implicit function theorem, the ELM neural network is introduced to approximate the uncertain items of the system, which simplifies the repeated adjustment process of the network training parameters. Meanwhile, the minimum learning parameter algorithm (MLP) is adopted to design the adaptive law for the norm of the network weight vector, which significantly reduces calculations. And it is theoretically proved that the closed-loop control system is stable and the tracking error is bounded. Finally, the effectiveness of the designed controller is verified by simulation.
机译：对于非共聚合的纯反馈系统，本文提出了一种基于极端学习机（ELM）的自适应神经控制方法。与现有方法不同，该方案首先将原始系统转换为仅通过等效变换仅含有一个未知项的非共进系统，从而避免了传统的反静电方法的麻烦和复杂的间接设计过程。其次，设计了高性能有限时间收敛 - 微分器（FD），通过该分化器，系统状态变量及其衍生物被精确地估计，以确保控制效果。第三，基于隐式函数定理，引入ELM神经网络以近似系统的不确定项目，这简化了网络训练参数的重复调整过程。同时，采用最低学习参数算法（MLP）来设计网络权重向量的标准的自适应法，这显着降低了计算。并且理论上证明了闭环控制系统是稳定的，并且界限误差。最后，通过模拟验证了所设计的控制器的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第13期|5613212.1-5613212.13|共13页
  • 作者

    Xu Chenyang; Lei Humin; Li Jiong; Ye Jikun; Zhang Dongyang;

  • 作者单位

    Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China;

    Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China;

    Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China;

    Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China;

    Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China;

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