Iterative learning-based robotic controller with prescribed human-robot interaction force

Xing, Xueyan; Maqsood, Kamran; Huang, Deqing; Yang, Chenguang; Li, Yanan

Abstract

In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb movement is first learned by the robot using the measurable interaction force, a recursive least square (RLS)-based estimator, and the Adam optimization method. Then, the desired trajectory of the robot can be obtained, tracking which the robot can supply the human's upper limb with a prescribed interaction force. Using this controller, the robot automatically adjusts its reference trajectory to embrace the differences between different human users with diverse degrees of upper limb movement characteristics. By designing a performance index in the form of interaction force integral, potential adverse effects caused by the time-related uncertainty during the learning process can be addressed. The experimental results demonstrate the effectiveness of the proposed method in supplying the prescribed interaction force to the human user.

• Journal Article Type: Article
• Acceptance Date: Oct 6, 2021
• Online Publication Date: Oct 25, 2021
• Publication Date: 2022-10
• Deposit Date: Nov 1, 2021
• Publicly Available Date: Nov 2, 2021
• Journal: IEEE Transactions on Automation Science and Engineering
• Print ISSN: 1545-5955
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 4
• Article Number: 9585226
• Pages: 3395-3408
• DOI: https://doi.org/10.1109/tase.2021.3119400
• Keywords: Electrical and Electronic Engineering; Control and Systems Engineering
• Public URL: https://uwe-repository.worktribe.com/output/8044347
• Publisher URL: https://ieeexplore.ieee.org/document/9585226