Skip to main content
SpringerLink
Log in

Hysteresis Modeling and Compensation Control of Soft Gripper

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13013))

Included in the following conference series:

  • 3700 Accesses

Abstract

In order to make full use of the soft robot's characteristics of flexibility and flexibility, combined with the hysteresis model, the air pressure-position hysteresis phenomenon in the soft actuator is studied, and the hysteresis compensation control method of the soft actuator is proposed. First, on the basis of the classic Prandtl-Ishlinskii (PI) model, an improved PI model and a mathematical calculation method for parameter identification are proposed; Secondly, the physical structure of the soft actuator is described, and the hysteresis data is obtained through experimental measurement; Finally, the two models used are simulated and analyzed, as well as the experimental verification of the entire system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lee, C., et al.: Soft robot review. Int. J. Control Autom. Syst. 15(1), 3–15 (2016). https://doi.org/10.1007/s12555-016-0462-3

    Article  Google Scholar 

  2. Polygerinos, P., Correll, N., Morin, S.A., et al.: Soft robotics: review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction. Adv. Eng. Mater. 19(12), 1700016 (2017)

    Article  Google Scholar 

  3. Bartlett, N.W., Tolley, M.T., Overvelde, J.T.B., et al.: A 3D-printed, functionally graded soft robot powered by combustion. Science 349(6244), 161–165 (2015)

    Article  Google Scholar 

  4. Cortez-Vega, R., Chairez, I., Feliu-Batlle, V.: Multi-link endoscopic manipulator robot actuated by shape memory alloys spring actuators controlled by a sliding mode. ISA Trans. (2020)

    Google Scholar 

  5. Costa, N., Caldwell, D.G.: Control of a biomimetic “soft-actuated” 10DoF lower body exoskeleton. IEEE (2006)

    Google Scholar 

  6. Skorina, E.H., Luo, M., Tao, W., et al.: Adapting to flexibility: model reference adaptive control of soft bending actuators. IEEE Robot. Autom. Lett. 2(2), 964–970 (2017)

    Article  Google Scholar 

  7. Gerboni, G., Diodato, A., Ciuti, G., et al.: Feedback control of soft robot actuators via commercial flex bend sensors. IEEE/ASME Trans. Mechatron. 22(4), 1881–1888 (2017)

    Article  Google Scholar 

  8. Xu, F., Jiang, Q., et al.: Design and testing of a soft robot with variable stiffness based on jamming principles. J. Mech. Eng. 56(23), 67–77 (2020)

    Article  Google Scholar 

  9. Zheng, G., Zhou, Y., Ju, M.: Robust control of a silicone soft robot using neural networks. ISA Trans. 100, 38–45 (2020)

    Article  Google Scholar 

  10. Thuruthel, T.G., et al., Soft robot perception using embedded soft sensors and recurrent neural networks. Sci. Robot. 4(26) (2019)

    Google Scholar 

  11. Massari, L., et al.: A Machine-learning-based approach to solve both contact location and force in soft material tactile sensors. Soft Robot. 7(4), 409–420 (2020)

    Article  MathSciNet  Google Scholar 

  12. Reinhart, R., Shareef, Z., Steil, J.: Hybrid analytical and data-driven modeling for feed-forward robot control. Sensors 17(2), 311 (2017). https://doi.org/10.3390/s17020311

    Article  Google Scholar 

  13. Zhang, Y., Gao, J., Yang, H., Hao, L.: A novel hysteresis modelling method with improved generalization capability for pneumatic artificial muscles. Smart Mater. Struct. 28(10), 105014 (2019). https://doi.org/10.1088/1361-665X/ab3770

    Article  Google Scholar 

  14. Abbasi, P., Nekoui, M., Zareinejad, M., Abbasi, P., Azhang, Z.: Position and force control of a soft pneumatic actuator. Soft Robot. 7(5), 550–563 (2020). https://doi.org/10.1089/soro.2019.0065

    Article  Google Scholar 

  15. Zakerzadeh, M.R., Sayyaadi, H.: Precise position control of shape memory alloy actuator using inverse hysteresis model and model reference adaptive control system. Mechatron. (Oxford) 23(8), 1150–1162 (2013)

    Article  Google Scholar 

  16. Vo-Minh, T., Tjahjowidodo, T., Ramon, H., Van Brussel, H.: A new approach to modeling hysteresis in a pneumatic artificial muscle using the Maxwell-slip model. IEEE/ASME Trans. Mechatron. 16(1), 177–186 (2011). https://doi.org/10.1109/TMECH.2009.2038373

    Article  Google Scholar 

  17. Lin, C., et al.: Hysteresis modeling and tracking control for a dual pneumatic artificial muscle system using Prandtl-Ishlinskii model. Mechatron. (Oxford) 28, 35–45 (2015)

    Article  Google Scholar 

Download references

Acknowledgment

This research is supported by National Natural Science Foundation of China (51775284), Primary Research & Developement Plan of Jiangsu Province (BE2018734), Joint Research Fund for Overseas Chinese, Hong Kong and Macao Young Scholars (61728302)and Postgraduate Research &Practice Innovation Program of Jiangsu Province (SJCX20_0253).

Author information

Authors and Affiliations

  1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Zhou Yi, Kaiwei Ma, Yang Sen & Fengyu Xu

  2. Jiangsu Engineering Lab for IOT Intelligent Robots (IOT Robot), Nanjing, 210023, China

    Zhou Yi, Kaiwei Ma, Yang Sen & Fengyu Xu

Authors
  1. Zhou Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaiwei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fengyu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Xin-Jun Liu

  2. Tsinghua University, Beijing, China

    Zhenguo Nie

  3. Beihang University, Beijing, China

    Jingjun Yu

  4. Tsinghua University, Beijing, China

    Fugui Xie

  5. Shandong University, Shandong, China

    Rui Song

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yi, Z., Ma, K., Sen, Y., Xu, F. (2021). Hysteresis Modeling and Compensation Control of Soft Gripper. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13013. Springer, Cham. https://doi.org/10.1007/978-3-030-89095-7_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89095-7_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89094-0

  • Online ISBN: 978-3-030-89095-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation