基于神经网络的高阶非线性延迟系统自适应控制设计

Jimin Yu, Baohua Wu, Shangbo Zhou
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引用次数: 0

摘要

本文研究了块三角型高阶非线性时滞系统的控制问题。采用径向基神经网络(RBF NN)逼近系统动力学中的未知非线性函数。Lyapunov-Krasovskii泛函用于补偿延迟项的影响。然后采用逆推递归方法设计了自适应神经网络输出跟踪控制器。基于Lyapunov稳定性理论和定理1,所提出的控制器可以保证所有闭环信号是全局、一致和最终有界的,并证明了这一点,同时输出跟踪收敛到原点的一个邻域。最后,通过仿真算例验证了理论结果的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Control Design for Higher Order Nonlinear Delay Systems Based on Neural Network
This paper focuses on the control of high-order nonlinear time-delay systems in block-triangular form. The RBF NN (radial basis function neural network) is chosen to approximate the unknown nonlinear functions in the system dynamics. Lyapunov-Krasovskii functionals are used to compensate the influence of delay terms. Then an adaptive neural network output tracking controller is designed by using the back-stepping recursive method. Based on Lyapunov stability theory and Theorem 1, the proposed controller can guarantee all closed-loop signals are globally, uniformly and ultimately bounded, which is proved, while the output tracking converges to a neighborhood of the origin. Finally, a simulation example is given to illustrate the correctness of the theoretical results.
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