Adaptive control of mechanical systems using neural networks

被引：13
|
作者：
Huang, Sunan [1 ]
Tan, Kok Kiong [1 ]
Lee, Tong Heng [1 ]
Putra, Andi Sudjana [1 ]
机构：
[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2007年 / 37卷 / 05期
关键词：
adaptive control; decentralized control; mechanical; systems; neural networks (NNs);
D O I：
10.1109/TSMCC.2007.900660
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we consider the decentralized adaptive control design problem for uncertain mechanical systems, where uncertainty may arise due to isolated subsystem and/or interconnections among subsystems. Radial basis function neural networks are used to approximate the nonlinear functions to include both dynamic and interconnection uncertainties in each subsystem. The stability of the thus designed control system can be guaranteed by a rigid proof. Finally, a simulation example is given to illustrate the effectiveness of the proposed algorithm.
引用
收藏
页码：897 / 903
页数：7
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