Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation

He, Wei; Sun, Yongkun; Yan, Zichen; Yang, Chenguang; Li, Zhijun; Kaynak, Okyay

Abstract

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies.

• Journal Article Type: Article
• Acceptance Date: Jun 2, 2019
• Online Publication Date: Aug 13, 2019
• Publication Date: May 1, 2020
• Deposit Date: Oct 29, 2019
• Publicly Available Date: Oct 29, 2019
• Journal: IEEE Transactions on Neural Networks and Learning Systems
• Print ISSN: 2162-237X
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 31
• Issue: 5
• Pages: 1735-1746
• DOI: https://doi.org/10.1109/tnnls.2019.2923241
• Keywords: Computer Networks and Communications; Software; Artificial Intelligence; Computer Science Applications
• Public URL: https://uwe-repository.worktribe.com/output/4189860