Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands

Huang, Darong; Yang, Chenguang; Ju, Zhaojie; Dai, Shi-Lu

Abstract

Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex environment with high-frequency disturbance. Thereby, to enhance tracking performance in a teleoperation system, only traditional DOB technique is insufficient. In this paper, for the purpose of constructing a feasible teleoperation scheme, we develop a novel controller that contains a variable gain scheme to deal with fast-time varying perturbation, whose gain is adjusted linearly according to human surface electromyographic signals collected from Myo wearable armband. In addition, for tracking the motion of operator’s arm, we derive five-joint-angle data of a moving human arm through two groups of quaternions generated from the armbands. Besides, the radial basis function neural networks and the disturbance observer-based control (DOBC) approaches are fused together into the proposed controller to compensate the unknown dynamics uncertainties of the slave robot as well as environmental perturbation. Experiments and simulations are conducted to demonstrated the effectiveness of the proposed strategy.

Citation

Huang, D., Yang, C., Ju, Z., & Dai, S. (2020). Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands. Autonomous Robots, 44(7), 1217-1231. https://doi.org/10.1007/s10514-020-09928-7

• Journal Article Type: Article
• Acceptance Date: Jun 19, 2020
• Online Publication Date: Jul 15, 2020
• Publication Date: Sep 1, 2020
• Deposit Date: Oct 1, 2020
• Publicly Available Date: Oct 5, 2020
• Journal: Autonomous Robots
• Print ISSN: 0929-5593
• Electronic ISSN: 1573-7527
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 44
• Issue: 7
• Pages: 1217-1231
• DOI: https://doi.org/10.1007/s10514-020-09928-7
• Keywords: Artificial Intelligence, Disturbance observer, Motion capture, Radial basis function neural networks, Teleoperation, Variable gain control
• Public URL: https://uwe-repository.worktribe.com/output/6738257
• Additional Information: Received: 11 October 2019; Accepted: 19 June 2020; First Online: 15 July 2020