Force Sensorless Admittance Control with Neural Learning for Robots with Actuator Saturation

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

Abstract

© 1982-2012 IEEE. In this paper, we present a sensorless admittance control scheme for robotic manipulators to interact with unknown environments in the presence of actuator saturation. The external environment is defined as linear models with unknown dynamics. Using admittance control, the robotic manipulator is controlled to be compliant with external torque from the environment. The external torque acted on the end-effector is estimated by using a disturbance observer based on generalized momentum. The model uncertainties are solved by using radial basis neural networks (NNs). To guarantee the tracking performance and tackle the effect of actuator saturation, an adaptive NN controller integrating an auxiliary system is designed to handle the actuator saturation. By employing Lyapunov stability theory, the stability of the closed-loop system is achieved. The experiments on the Baxter robot are implemented to verify the effectiveness of the proposed method.

Citation

Peng, G., Yang, C., He, W., & Chen, C. L. (2020). Force Sensorless Admittance Control with Neural Learning for Robots with Actuator Saturation. IEEE Transactions on Industrial Electronics, 67(4), 3138-3148. https://doi.org/10.1109/TIE.2019.2912781

• Journal Article Type: Article
• Acceptance Date: Mar 25, 2019
• Online Publication Date: Apr 29, 2019
• Publication Date: Apr 1, 2020
• Deposit Date: May 9, 2019
• Publicly Available Date: May 9, 2019
• Journal: IEEE Transactions on Industrial Electronics
• Print ISSN: 0278-0046
• Electronic ISSN: 1557-9948
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 67
• Issue: 4
• Pages: 3138-3148
• DOI: https://doi.org/10.1109/TIE.2019.2912781
• Keywords: adaptive neural control, observer, neural networks (NNs),; admittance control
• Public URL: https://uwe-repository.worktribe.com/output/847979
• Publisher URL: http://doi.org/10.1109/TIE.2019.2912781
• Additional Information: Additional Information : (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.