A neural-network compensator with fuzzy robustification terms for improved design of adaptive control of robot manipulators

被引：0
|
作者：
Fung, Y.H. [1 ]
Tso, S.K. [1 ]
机构：
[1] Ctr. Intelligent Des., Automat./Mfg., City University of Hong Kong, Hong Kong, Hong Kong
来源：
System and Control: Theory and Applications | 2000年
关键词：
Adaptive control systems - Algorithms - Fuzzy sets - Gravitational effects - Manipulators - Radial basis function networks - Robot applications - Robustness (control systems);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A sum radial-basis-function neural-network (NN) compensator with computed-torque control and novel weight-tuning algorithms is proposed to improve tracking performance and to account for structured/unstructured uncertainties of robot manipulators. The proposed weight-tuning algorithms do not require the initial NN weights to be small. The bounds of NN weights are guaranteed to be convergent in the sense of Lyapunov. The effectiveness of the proposed algorithm is demonstrated using a laboratory robot manipulator.
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页码：119 / 124
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