Neural adaptive global stability control for robot manipulators with time-varying output constraints

Fan, Yongqing; Kang, Tongtong; Wang, Wenqing; Yang, Chenguang

Abstract

© 2019 John Wiley & Sons, Ltd. In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time-varying parameter that installed in the fore-end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time-varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time-varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods.

• Journal Article Type: Article
• Acceptance Date: Jul 4, 2019
• Online Publication Date: Aug 5, 2019
• Publication Date: Nov 10, 2019
• Deposit Date: Aug 26, 2019
• Publicly Available Date: Aug 6, 2020
• Journal: International Journal of Robust and Nonlinear Control
• Print ISSN: 1049-8923
• Electronic ISSN: 1099-1239
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 16
• Pages: 5765-5780
• DOI: https://doi.org/10.1002/rnc.4690
• Keywords: Control and Systems Engineering; Mechanical Engineering; Electrical and Electronic Engineering; Industrial and Manufacturing Engineering; General Chemical Engineering; Aerospace Engineering; Biomedical Engineering
• Public URL: https://uwe-repository.worktribe.com/output/2489084
• Additional Information: Received: 2018-07-06; Accepted: 2019-07-04; Published: 2019-08-05