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A feedforward compensation approach for cable-driven musculoskeletal systems

Published online by Cambridge University Press:  21 December 2022

Yerui Fan Open the ORCID record for Yerui Fan [Opens in a new window] ,
Jianbo Yuan Open the ORCID record for Jianbo Yuan [Opens in a new window] ,
Yaxiong Wu  and
Hong Qiao
Yerui Fan
Affiliation:
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Jianbo Yuan
Affiliation:
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Yaxiong Wu
Affiliation:
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Hong Qiao*
Affiliation:
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

This paper presents a feedforward compensation approach for musculoskeletal systems (MSs). Compared with traditional rigid robots, human arms have the advantages of flexibility and safety in operation in unstructured environments. However, the influence of external unknown disturbances, inner friction effects, and dynamic uncertainties of the MS makes it difficult to model and practically apply. In order to reduce the inner friction effects of the hardware platform and the over-relaxation/tension of the cable-pull drive, a feedforward friction compensation method for the cable-pulled artificial muscle unit is proposed. The method analyzes the friction causes of the hardware structure and establishes a mapping network relationship between the joint variables and the muscle force error in the muscle space. The experimental results show that the method can effectively improve the control accuracy and reduce the artificial muscle over-relaxation/tension instability.

Keywords

biomimetic robotscontrol of robotic systemsforce controlhumanoid robotsserial manipulator design and kinematics
Type
Research Article
Information
Robotica , Volume 41 , Issue 4 , April 2023 , pp. 1221 - 1230
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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