Stable adaptive tracking of uncertain systems using nonlinearly parametrized on-line approximators
Stable adaptive tracking of uncertain systems using nonlinearly parametrized on-line approximators
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DOI：
10.1080/002071798222280
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发表时间：
1998
影响因子：
2.1
通讯作者：
M. Polycarpou;M. Mears
中科院分区：
文献类型：
--
作者：
M. Polycarpou;M. Mears
研究了具有未知非线性的不确定非线性动力系统的稳定自适应神经网络控制器的设计问题。李雅普诺夫综合方法是用来开发状态反馈自适应控制方案的基础上一般类的非线性参数化在线近似模型。关键的假设是，系统的不确定性满足严格的反馈条件和网络重构误差和高阶项的在线逼近（相对于网络权重）满足一定的边界条件。一个自适应定界设计表明，整个神经控制系统保证半全局一致的最终有界的零跟踪误差的邻域内。通过仿真实例说明了理论结果。
The design of stable adaptive neural controllers for uncertain nonlinear dynamical systems with unknown nonlinearities is considered. The Lyapunov synthesis approach is used to develop state-feedback adaptive control schemes based on a general class of nonlinearly parametrized on-line approximation models. The key assumptions are that the system uncertainty satisfies a strict feedback condition and that the network reconstruction error and higher-order terms of the on-line approximator (with respect to the network weights) satisfy certain bounding conditions. An adaptive bounding design is used to show that the overall neural control system guarantees semi-global uniform ultimate boundedness within a neighbourhood of zero tracking error. The theoretical results are illustrated through a simulation example.