Adaptive neural network control of nonlinear systems with unknown time delays
Adaptive neural network control of nonlinear systems with unknown time delays
复制标题
DOI：
10.1109/tac.2003.819287
复制
发表时间：
2003-11-01
影响因子：
6.8
通讯作者：
Lee, TH
中科院分区：
文献类型：
--
作者：
Ge, SS;Hong, F;Lee, TH
针对一类具有未知时滞的严格反馈非线性系统，提出了自适应神经网络控制方法。使用适当的Lyapunov-Krasovskii泛函，补偿未知时间延迟的不确定性，以便可以进行迭代反推设计。此外，控制器奇异性问题的解决，通过使用积分李雅普诺夫函数和采用实用的鲁棒神经网络控制。在实际紧集上保证了神经网络逼近未知系统函数的可行性。证明了所提出的系统Backstepping设计方法能够保证闭环系统中所有信号的半球一致最终有界性，并证明了跟踪误差收敛到原点的一个小邻域内.
In this note, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. Using appropriate Lyapunov-Krasovskii functionals, the uncertainties of unknown time delays are compensated for such that iterative backstepping design can be carried out. In addition, controller singularity problems are solved by using the integral Lyapunov function and employing practical robust neural network control. The feasibility of neural network approximation of unknown system functions is guaranteed over practical compact sets. It is proved that the proposed systematic backstepping design method is able to guarantee semiglobally uniformly ultimate boundedness of all the signals in the closed-loop system and the tracking error is proven to converge to a small neighborhood of the origin.