Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks

被引:224
作者
Hovakimyan, N [1 ]
Nardi, F
Calise, A
Kim, N
机构
[1] Georgia Inst Technol, Sch Aerosp Engn, Atlanta, GA 30332 USA
[2] Fiat Auto R&D, Farmington Mills, MI 48331 USA
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2002年 / 13卷 / 06期
关键词
nonlinear adaptive control; output feedback; parametric uncertainty; single-hidden-layer neural networks (SHL NNs); unmodeled dynamics;
D O I
10.1109/TNN.2002.804289
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
We consider adaptive output feedback control of uncertain nonlinear systems, in which both the dynamics and the dimension of the regulated system may be unknown. However, the relative degree of the regulated output is assumed to be known. Given a smooth reference trajectory, the problem is to design a controller that forces the system measurement to track it with bounded errors. The classical approach requires a state observer. Finding a good observer for an uncertain nonlinear system is not an obvious task. We argue that it is sufficient to build an observer for the output tracking error. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. The theoretical results are illustrated in the design of a controller for a fourth-order nonlinear system of relative degree two and a high-bandwidth attitude command system for a model R-50 helicopter.
引用
收藏
页码:1420 / 1431
页数:12
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