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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: hong.qiao@ia.ac.cn
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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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References

Mizuuchi, I., Nakanishi, Y., Sodeyama, Y., Namiki, Y., Nishino, T., Muramatsu, N., Urata, J., Hongo, K., Yoshikai, T. and Inaba, M., “An Advanced Musculoskeletal Humanoid Kojiro,” In: International Conference on Humanoid Robots, (2007) pp. 294–299.Google Scholar
Li, Z., Xu, C., Wei, Q., Shi, C. and Su, C., “Human-inspired control of dual-arm exoskeleton robots with force and impedance,” IEEE Trans. Syst. Man Cybern. Syst. 50(12), 5296–5305 (2020).CrossRefGoogle Scholar
Li, G., Li, Z. and Kan, Z., “Assimilation control of a robotic exoskeleton for physical human-robot,” Interact. IEEE Robot. Autom. Lett. 7(2), 2977–2984 (2022).CrossRefGoogle Scholar
Kawamura, M., Ookubo, S., Asano, Y., Kozuki, T., Okada, K. and Inaba, M., “A Joint Space Controller Based on Redundant Muscle Tension for Multiple DOF Joints in Musculoskeletal Humanoids,” In: International Conference on Humanoid Robots (2016) pp. 814–819.Google Scholar
Kawaharazuka, K., Koga, Y., Tsuzuki, K., Onitsuka, M., Asano, Y., Okada, K., Kawasaki, K. and Inaba, M., “Exceeding the Maximum Speed Limit of the Joint Angle for the Redundant Tendon-Driven Structures of Musculoskeletal Humanoids,” In: IEEE International Conference on Intelligent Robots and Systems (2020) pp. 3585–3590.Google Scholar
Kawaharazuka, K., Makino, S., Kawamura, M., Asano, Y., Okada, K. and Inaba, M., “A Method of Joint Angle Estimation Using Only Relative Changes in Muscle Lengths for Tendon Driven Humanoids with Complex Musculoskeletal Structures,” In: 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) (2018) pp. 1128–1135.Google Scholar
Jantsch, M., Wittmeier, S., Dalamagkidis, K., Panos, A., Volkart, F., Knoll, A. and Anthrob, A Printed Anthropomimetic Robot,” In: 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids) (2013) pp. 342–347.Google Scholar
Asano, Y., Okada, K. and Inaba, M., “Design principles of a human mimetic humanoid: Humanoid platform to study human intelligence and internal body system,” Sci. Robot. 2(13), eaaq0899 (2017).CrossRefGoogle ScholarPubMed
Wei, H., Qiu, Y. and Sheng, Y., “On the cable pseudo-drag problem of cable-Driven parallel camera robots at high speeds,” Robotica 37(10), 1695–1709 (2019).CrossRefGoogle Scholar
Wittmeier, S., Alessandro, C., Bascarevic, N., Dalamagkidis, K., Devereux, D., Diamond, A., Jäntsch, M., Jovanovic, K., Knight, R., Marques, H. G., Milosavljevic, P., Mitra, B., Svetozarevic, B., Potkonjak, V., Pfeifer, R., Knoll, A. and Holland, O., “Toward anthropomimetic robotics: Development, simulation, and control of a musculoskeletal torso,” Artif. Life 19(1), 171–193 (2013).CrossRefGoogle ScholarPubMed
Kim, U., Jung, D., Jeong, H., Park, J., Jung, H., Cheong, J., Choi, H. R., Do, H. and Park, C., “Integrated linkage-driven dexterous anthropomorphic robotic hand,” Nat. Commun. 12(1), 1–13 (2021).CrossRefGoogle ScholarPubMed
Lischinsky, P., Canudas-de-Wit, C. and Morel, G., “Friction compensation for an industrial hydraulic robot,” IEEE Cont. Syst. 19(1), 25–32 (1999).Google Scholar
Jin, B., Sun, C., Zhang, A., Ding, N., Lin, J., Deng, G., Zhu, Z. and Sun, Z., “Joint Torque Estimation Toward Dynamic and Compliant Control for Gear-Driven Torque Sensorless Quadruped Robot,” In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019) pp. 4630–4637.Google Scholar
Katz, B., Carlo, J. D. and Kim, S., “Mini Cheetah A Platform for Pushing the Limits of Dynamic Quadruped Control,” In: International Conference on Robotics and Automation (ICRA) (2019) pp. 6295–6301.Google Scholar
Zhao, T., Liu, Y., Li, Z., Su, C. and Feng, Y., “Adaptive control and optimization of mobile manipulation subject to input saturation and switching constraints,” IEEE Trans. Autom. Sci. Eng. 16(4), 1543–1555 (2019).CrossRefGoogle Scholar
Kawaharazuka, K., Kawamura, M., Makino, S., Asano, Y., Okada, K. and Inaba, M., “Antagonist inhibition control in redundant tendon-driven structures based on human reciprocal innervation for wide range limb motion of musculoskeletal humanoids,” IEEE Robot. Automat. Lett. 2(4), 2119–2126 (2017).CrossRefGoogle Scholar
Yuan, J., Wu, Y., Wang, B. and Qiao, H., “Musculoskeletal Robot with Motor Driven Artificial Muscle,” In: 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) (2021) pp. 448–452.Google Scholar
Su, H., Qi, W., Hu, Y., Sandoval, J., Zhang, L., Schmirander, Y., Chen, G., Aliverti, A., Knoll, A., Ferrigno, G. and De Momi, E., “Towards model-free tool dynamic identification and calibration using multi-layer neural network,” Sensors 19(17), 3636 (2019).CrossRefGoogle ScholarPubMed
Runge, G., Wiese, M. and Raatz, A., “FEM-based Training of Artificial Neural Networks for Modular Soft Robots,” In: 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO) (2017) pp. 385–392.Google Scholar
Mizuuchi, I., Yoshikai, T., Nakanishi, Y., Sodeyama, Y., Yamamoto, T., Miyadera, A., Niemela, T., Hayashi, M., Urata, J. and Inaba, M., “Development of Muscle-Driven Flexible-Spine Humanoids,” In: 5th IEEE-RAS International Conference on Humanoid Robots (2005) pp. 339–344.Google Scholar
Wu, Y., Chen, J. and Qiao, H., “Anti-interference analysis of bio-inspired musculoskeletal robotic system,” Neurocomputing 436, 114–125 (2021).CrossRefGoogle Scholar
Hill, T. L., “Theoretical formalism for the sliding filament model of contraction of striated muscle Part I,” Prog. Biophys. Mol. Biol. 28, 267–340 (1974).CrossRefGoogle ScholarPubMed