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

Abstract

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.

Citation

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. https://doi.org/10.1080/00207721.2019.1567863

• 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
• DOI: https://doi.org/10.1080/00207721.2019.1567863
• Public URL: https://uwe-repository.worktribe.com/output/855101
• Publisher URL: http://doi.org/10.1080/00207721.2019.1567863
• 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: http://doi.org/10.1080/00207721.2019.1567863.