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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Learning From ISS-Modular Adaptive NN Control of Nonlinear Strict-Feedback Systems
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Learning From ISS-Modular Adaptive NN Control of Nonlinear Strict-Feedback Systems

机译：非线性严格反馈系统的ISS模块自适应NN控制学习

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摘要

This paper studies learning from adaptive neural control (ANC) for a class of nonlinear strict-feedback systems with unknown affine terms. To achieve the purpose of learning, a simple input-to-state stability (ISS) modular ANC method is first presented to ensure the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in finite time. Subsequently, it is proven that learning with the proposed stable ISS-modular ANC can be achieved. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using existing methods. To overcome these difficulties, the stable closed-loop system in the control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation condition for the radial basis function neural network (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop system dynamics is achieved in a local region along recurrent orbits of closed-loop signals, and learning is implemented during a closed-loop feedback control process. The learned knowledge is reused to achieve stability and an improved performance, thereby avoiding the tremendous repeated training process of NNs. Simulation studies are given to demonstrate the effectiveness of the proposed method.
机译：本文研究了一类具有仿射项未知的非线性严格反馈系统的自适应神经控制（ANC）学习。为了达到学习的目的，首先提出一种简单的输入至状态稳定性（ISS）模块化ANC方法，以确保闭环系统中所有信号的有界性和跟踪误差在有限时间内的收敛性。随后，证明了利用所提出的稳定的ISS模块化ANC可以实现学习。所考虑系统的级联结构和未知仿射项使得使用现有方法很难学习。为了克服这些困难，将控制过程中的稳定闭环系统分解为具有适当状态变换的一系列线性时变（LTV）扰动子系统。使用递归设计，建立了径向基函数神经网络（NN）的部分持久激励条件，该条件保证了LTV扰动子系统的指数稳定性。因此，在沿着闭环信号的重复轨道的局部区域中实现了闭环系统动力学的精确近似，并且在闭环反馈控制过程中实现了学习。所学习的知识被重用以实现稳定性和改进的性能，从而避免了神经网络的大量重复训练过程。仿真研究证明了该方法的有效性。

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