Neural-Learning-Based Telerobot Control with Guaranteed Performance

Yang, Chenguang; Wang, Xinyu; Cheng, Long; Ma, Hongbin

Abstract

© 2013 IEEE. In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

• Journal Article Type: Article
• Acceptance Date: May 22, 2016
• Online Publication Date: Jun 21, 2016
• Publication Date: Oct 1, 2017
• Deposit Date: Oct 8, 2019
• Publicly Available Date: Oct 8, 2019
• Journal: IEEE Transactions on Cybernetics
• Print ISSN: 2168-2267
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 47
• Issue: 10
• Pages: 3148-3159
• DOI: https://doi.org/10.1109/TCYB.2016.2573837
• Keywords: Control and Systems Engineering; Human-Computer Interaction; Electrical and Electronic Engineering; Software; Information Systems; Computer Science Applications; Manipulator dynamics; Robot sensing systems; Robot kinematics; Kinematics; Collision avoidanc
• Public URL: https://uwe-repository.worktribe.com/output/3596726