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An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions

Xia, Jun; Zhang, Yujia; Yang, Chenguang; Wang, Min; Annamalai, Andy


Jun Xia

Yujia Zhang

Min Wang

Andy Annamalai


Conventional Neural Network (NN) control for robots uses radial basis function (RBF) and for n-link robot with online control, the number of nodes and weighting matrix increases exponentially, which requires a number of calculations to be performed within a very short duration of time. This consumes a large amount of computational memory and may subsequently result in system failure. To avoid this problem, this paper proposes an innovative NN robot control using a dimension compressed RBF (DCRBF) for a class of n-degree of freedom (DOF) robot with full-state constraints. The proposed DCRBF NN control scheme can compress the nodes and weighting matrix greatly and provide an output that meets the prescribed tracking performance. Additionally, adaption laws are designed to compensate for the internal and external uncertainties. Finally, the effectiveness of the proposed method has been verified by simulations. The results indicate that the proposed method, integral Barrier Lyapunov Functions (iBLF), avoids the existing defects of Barrier Lyapunov Functions (BLF) and prevents the constraint violations.


Xia, J., Zhang, Y., Yang, C., Wang, M., & Annamalai, A. (2019). An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions. International Journal of Systems Science, 50(3), 638-651.

Journal Article Type Article
Acceptance Date Jan 5, 2019
Online Publication Date Jan 24, 2019
Publication Date Sep 1, 2019
Deposit Date Feb 7, 2019
Publicly Available Date Jan 25, 2020
Journal International Journal of Systems Science
Print ISSN 0020-7721
Electronic ISSN 1464-5319
Publisher Taylor & Francis
Peer Reviewed Peer Reviewed
Volume 50
Issue 3
Pages 638-651
Public URL
Publisher URL
Additional Information Additional Information : This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 24th January 2019, available online:


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