Please use this identifier to cite or link to this item: https://doi.org/10.1163/156855303765203056
Title: Stable decentralized adaptive control design of robot manipulators using neural network approximations
Authors: Huang, S.N. 
Tan, K.K. 
Lee, T.H. 
Keywords: Adaptive control
Decentralized control
Neural networks
Radial basis function
System uncertainty
Issue Date: 2003
Source: Huang, S.N., Tan, K.K., Lee, T.H. (2003). Stable decentralized adaptive control design of robot manipulators using neural network approximations. Advanced Robotics 17 (4) : 369-383. ScholarBank@NUS Repository. https://doi.org/10.1163/156855303765203056
Abstract: 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.
Source Title: Advanced Robotics
URI: http://scholarbank.nus.edu.sg/handle/10635/57511
ISSN: 01691864
DOI: 10.1163/156855303765203056
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