Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays
Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays
复制标题
DOI：
10.1109/cdc.2009.5400312
复制
发表时间：
2009-12
期刊：
影响因子：
--
通讯作者：
Min Wang;S. Ge
中科院分区：
文献类型：
--
作者：
Min Wang;S. Ge
本文为一类具有多个未知状态时变延迟的非携带纯反馈系统提供了自适应神经跟踪控制。引入分离技术将所有时间变化的延迟状态的未知函数分解为每个延迟状态的一系列连续函数。一种新型的Lyapunov-Krasovskii功能用于补偿当前延迟状态的未知功能，该功能实际上没有对未知时间延迟函数的任何限制性假设。提出的控制方案确保了闭环系统中所有信号和跟踪性能的界限。提供了模拟研究以证明所提出的方案的有效性。
This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. A novel Lyapunov-Krasovskii functional is employed to compensate for the unknown function of current delayed state, which is effectively free from any restrictive assumption on unknown time-delay functions. The proposed control scheme guarantees the boundedness of all the signals in the closed-loop system and the tracking performance. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.