Wang Qiufeng, Zhang Zhengqiang, Xie Xue-Jun
IEEE Trans Neural Netw Learn Syst. 2023 Feb;34(2):814-823. doi: 10.1109/TNNLS.2021.3102274. Epub 2023 Feb 3.
This article investigates the problem of global neural network (NN) tracking control for uncertain nonlinear systems in output feedback form under disturbances with unknown bounds. Compared with the existing NN control method, the differences of the proposed scheme are as follows. The designed actual controller consists of an NN controller working in the approximate domain and a robust controller working outside the approximate domain, in addition, a new smooth switching function is designed to achieve the smooth switching between the two controllers, in order to ensure the globally uniformly ultimately bounded of all closed-loop signals. The Lyapunov analysis method is used to strictly prove the global stability under the combined action of unmeasured states and system uncertainties, and the output tracking error is guaranteed to converge to an arbitrarily small neighborhood through a reasonable selection of design parameters. A numerical example and a practical example were put forward to verify the effectiveness of the control strategy.
本文研究了在未知界干扰下输出反馈形式的不确定非线性系统的全局神经网络（NN）跟踪控制问题。与现有的神经网络控制方法相比，所提方案的不同之处如下。所设计的实际控制器由在近似域工作的神经网络控制器和在近似域之外工作的鲁棒控制器组成，此外，设计了一种新的平滑切换函数以实现两个控制器之间的平滑切换，从而确保所有闭环信号全局一致最终有界。利用李雅普诺夫分析方法严格证明了在未测量状态和系统不确定性共同作用下的全局稳定性，并且通过合理选择设计参数保证输出跟踪误差收敛到任意小的邻域。给出了一个数值例子和一个实际例子来验证控制策略的有效性。
