Neural-net based robust adaptive control of uncertain nonlinear composite systems

被引：0
|
作者：
Zhang, YX [1 ]
Dimirovski, GM [1 ]
Jing, YW [1 ]
Zhang, SY [1 ]
机构：
[1] NE Univ, Inst Control Theory, Shyenyang 110004, Liaoning, Peoples R China
来源：
CCA 2003: PROCEEDINGS OF 2003 IEEE CONFERENCE ON CONTROL APPLICATIONS, VOLS 1 AND 2 | 2003年
关键词：
composite similarity systems; neural networks; uncertain nonlinear systems; real stability; robust adaptive control;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new robust adaptive hybrid control design, employing both math-analytical model and neural networks, for a class of uncertain nonlinear composite systems possessing similarity property has been derived. This design technique makes an adequate use of the. structural characteristics of similar composite systems, and resolves the uncertainty issues in gains and interconnections by on-line updating the weights of the respective artificial neural nets. It depends on little a-priori knowledge and assures the system stability in closed-loop. It can be readily implemented within computer control systems, and it requires little computation effort and time. The application of this technique is illustrated by the real-world axis-tray drive mechatronic system.
引用
收藏
页码：819 / 825
页数：7
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