Stable decentralized adaptive control design of robot manipulators using neural network approximations

被引：4

作者：

Huang, SN ^{[1
]}

Tan, KK ^{[1
]}

Lee, TH ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117548, Singapore

来源：

ADVANCED ROBOTICS | 2003年 / 17卷 / 04期

关键词：

adaptive control; decentralized control; neural networks; radial basis function; system uncertainty;

D O I：

10.1163/156855303765203056

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

In this paper, we present a decentralized neural network (NN) adaptive technique for control of robot manipulators in the presence of unknown non-linear functions. Radial basis function NNs are used to approximate the non-linear functions to include the case of both parametric and dynamic uncertainty in each subsystem. The robustifying terms are added to the controllers to overcome the effects of the interconnections. The stability can be guaranteed by using a rigid proof. Finally, simulation is given to illustrate the effectiveness of the proposed algorithm.

引用

页码：369 / 383

页数：15

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