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Adaptive Impedance Decentralized Control of Modular Robot Manipulators for Physical Human-robot Interaction

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Abstract

For the problem of dynamic contact force tracking control under physical human-robot interaction (pHRI), we propose a dual closed-loop adaptive decentralized control framework. The dynamic model of modular robot manipulator (MRM) subsystem is established based on joint torque feedback (JTF) technology. On the basis of fully analyzing the model uncertainty, the method based on decomposition is used to dynamically compensate the model uncertainty. Using Lyapunov theory, the uniform and ultimate boundedness (UUB) of dynamic contact force tracking error and MRM position tracking error in pHRI process are confirmed. A neural network (NN) observer is designed to dynamically compensate the uncertainty of controller. Finally, the effectiveness of this method is verified by experiments.

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Data Availibility Statement

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant no. 62173047), the Scientific Technological Development Plan Project in Jilin Province of China (Grant no. 20220201038GX) and the Science and Technology project of Jilin Provincial Education Department of China during the 13th Five-Year Plan Period (Grant no. JJKH20220689KJ).

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Authors and Affiliations

  1. Department of Control Science and Engineering, Changchun University of Technology, 130012, Changchun, China

    Bo Dong, Yusheng Jing, Xinye Zhu, Yiming Cui & Tianjiao An

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  1. Bo Dong
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  2. Yusheng Jing
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  3. Xinye Zhu
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  4. Yiming Cui
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  5. Tianjiao An
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Contributions

Tianjiao An: Software. Bo Dong: Writing-original draft. Yusheng Jing: Supervision. Xinye Zhu: Conceptualization, Methodology. Yiming Cui: Data curation.

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Correspondence to Tianjiao An.

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Dong, B., Jing, Y., Zhu, X. et al. Adaptive Impedance Decentralized Control of Modular Robot Manipulators for Physical Human-robot Interaction. J Intell Robot Syst 109, 48 (2023). https://doi.org/10.1007/s10846-023-01978-0

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