Neural-learning-based force sensorless admittance control for robots with input deadzone

Peng, Guangzhu; Chen, C. L. Philip; He, Wei; Yang, Chenguang

Abstract

This paper presents a neural networks based admittance control scheme for robotic manipulators when interacting with the unknown environment in the presence of the actuator deadzone without needing force sensing. A compliant behaviour of robotic manipulators in response to external torques from the unknown environment is achieved by admittance control. Inspired by broad learning system (BLS), a flatted neural network structure using Radial Basis Function (RBF) with incremental learning algorithm is proposed to estimate the external torque, which can avoid retraining process if the system is modelled insufficiently. To deal with uncertainties in the robot system, an adaptive neural controller with dynamic learning framework is developed to ensure the tracking performance. Experiments on the Baxter robot have been implemented to test the effectiveness of the proposed method.

• Journal Article Type: Article
• Acceptance Date: Apr 17, 2020
• Online Publication Date: May 7, 2020
• Publication Date: Jun 1, 2021
• Deposit Date: May 10, 2020
• Publicly Available Date: May 11, 2020
• Journal: IEEE Transactions on Industrial Electronics
• Print ISSN: 0278-0046
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 68
• Issue: 6
• Pages: 5184-5196
• DOI: https://doi.org/10.1109/tie.2020.2991929
• Keywords: control and systems engineering, electrical and electronic engineering, neural networks (NNs) , adaptive control , broad learning , force/torque observer , admittance control
• Public URL: https://uwe-repository.worktribe.com/output/5972740