Skip to main content

Advertisement

SpringerLink
Log in

Model-based Control with Interaction Predicting for Human-coupled Lower Exoskeleton Systems

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Sensitivity Amplification Control (SAC) algorithm was first proposed in the augmentation application of the Berkeley Lower Extremity Exoskeleton (BLEEX). Since the SAC algorithm can greatly reduce the complexity of exoskeleton system, it is widely used in human augmentation applications. Nevertheless, the performance of the SAC algorithm depends on the accuracy of dynamic model parameters. In this paper, we propose a novel Model-based control with Interaction Predicting (MIP) strategy to lower dependency on the accurate dynamic model of the exoskeleton. The MIP consists of an interaction predictor and a model-based controller. The interaction predictor can predict motion trajectories of the pilot and substitute for the pilot to drive the exoskeleton through an impedance model. In proposed strategy, the model-based controller not only amplify the forces initiated by the interaction predictor, but more importantly the forces imposed by the pilot to correct the errors between the predictive motion trajectory and the intended motion trajectory of the pilot. Illustrative simulations and experimental results are presented to demonstrate the efficiency of the proposed strategy. Additionally, the comparisons with traditional model-based control algorithm are also presented to demonstrate the efficiency and superiority of the proposed control strategy for lowering dependency on dynamic models.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Calzado, J., Lindsay, A., Chen, C., Samuels, G., Olszewska, J.: SAMI: Interactive, multi-sense robot architecture. In: IEEE 22nd International Conference on Intelligent Engineering Systems (INES), pp. 317–322 (2018)

  2. Chen, Q., Cheng, H., Yue, C., Huang, R., Guo, H.: Step length adaptation for walking assistance. In: IEEE International Conference on Mechatronics and Automation (ICMA), pp. 644–650 (2017)

  3. Chen, Q., Cheng, H., Yue, C., Huang, R., Guo, H.: Dynamic balance gait for walking assistance exoskeleton. Applied Bionics and Biomechanics, pp. 1–10 (2018)

  4. Ding, Y., Galiana, I., Siviy, C., Panizzolo, F.A., Walsh, C.: IMU-based iterative control for hip extension assistance with a soft exosuit. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3501–3508 (2016)

  5. Ghan, J., Kazerooni, H.: System identification for the Berkeley Lower Extremity Exoskeleton (BLEEX). In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3477–3484 (2006)

  6. Ghan, J., Steger, R., Kazerooni, H.: Control and system identification for the Berkeley Lower Extremity Exoskeleton (BLEEX). Adv. Robot. 20(9), 989–1014 (2006)

    Article  Google Scholar 

  7. Huang, L., Steger, R.R., Kazerooni, H.: Hybrid control of the Berkeley Lower Extremity Exoskeleton (BLEEX). In: International Mechanical Engineering Congress and Exposition, pp. 1429–1436 (2005)

  8. Huang, R., Cheng, H., Chen, Q., Tran, H.T., Lin, X.: Interactive learning for sensitivity factors of a human-powered augmentation lower wxoskeleton. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 6409–6415 (2015)

  9. Huang, R., Cheng, H., Guo, H., Chen, Q., Lin, X.: Hierarchical interactive learning for a human-powered augmentation lower exoskeleton. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 257–263 (2016)

  10. Huang, R., Cheng, H., Guo, H., Lin, X., Chen, Q., Sun, F.: Learning cooperative primitives with physical human-robot interaction for a HUman-Powered Lower EXoskeleton. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 5355–5360 (2016)

  11. Huang, R., Cheng, H., Guo, H., Lin, X., Zhang, J.: Hierarchical learning control with physical human-exoskeleton interaction. Inform. Sci. 432, 584–595 (2018)

    Article  Google Scholar 

  12. Ijspeert, A.J., Nakanishi, J., Schaal, S.: Learning rhythmic movements by demonstration using nonlinear oscillators. In: IEEE International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp 958–963 (2002)

  13. Kazerooni, H., Chu, A., Steger, R.: That which does not stabilize, will only make us stronger. Int. J Robotics Res. 26(1), 75–89 (2007)

    Article  Google Scholar 

  14. Kazerooni, H., Racine, J.L., Huang, L., Steger, R.: On the control of the Berkeley Lower Extremity Exoskeleton (BLEEX). In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4353–4360 (2005)

  15. Kazerooni, H., Steger, R.: The Berkeley Lower Extremity Exoskeleton. Journal of Dynamic Systems, Measurement and Control 128(1), 14–25 (2006)

    Article  Google Scholar 

  16. Li, Z., Ma, W., Yin, Z., Guo, H.: Tracking control of time-varying knee exoskeleton disturbed by interaction torque. ISA Trans. 71, 458–466 (2017)

    Article  Google Scholar 

  17. Ng, A.Y., Coates, A., Diel, M., Ganapathi, V., Schulte, J., Tse, B., Berger, E., Liang, E.: Autonomous inverted helicopter flight via reinforcement learning. In: Experimental Robotics IX, pp 363–372. Springer (2006)

  18. Nguyen-Tuong, D., Peters, J.R., Seeger, M.: Local Gaussian process regression for real time online model learning. In: Advances in Neural Information Processing Systems, pp. 1193–1200 (2009)

  19. Olszewska, J.I., Houghtaling, M., Goncalves, P.J., Fabiano, N., Haidegger, T., Carbonera, J.L., Patterson, W.R., Ragavan, S.V., Fiorini, S.R., Prestes, E.: Robotic standard development life cycle in action. Journal of Intelligent & Robotic Systems, pp. 1–13 (2019)

  20. Schaal, S., Atkeson, C.G.: Constructive incremental learning from only local information. Neural Comput. 10(8), 2047–2084 (1998)

    Article  Google Scholar 

  21. Steger, R., Kim, S.H., Kazerooni, H.: Control scheme and networked control architecture for the Berkeley Lower Extremity Exoskeleton (BLEEX). In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3469–3476 (2006)

  22. Tran, H.T., Cheng, H., Lin, X., Duong, M.K., Huang, R.: The relationship between physical human-exoskeleton interaction and dynamic factors: using a learning approach for control applications. Science China Information Sciences 57(12), 1–13 (2014)

    Article  Google Scholar 

  23. Tutsoy, O.: CPG based RL algorithm learns to control of a humanoid robot leg. Int. J. Robot. Autom. 30(2), 1–7 (2015)

    Google Scholar 

  24. Walsh, C.J., Paluska, D., Pasch, K., Grand, W., Valiente, A., Herr, H.: Development of a lightweight, underactuated exoskeleton for load-carrying augmentation. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3485–3491 (2006)

  25. Wang, Y., Gao, F., Doyle, F.J. III: Survey on iterative learning control, repetitive control, and run-to-run control. J. Process. Control. 19(10), 1589–1600 (2009)

    Article  Google Scholar 

  26. Winter, D.A.: Biomechanics and Motor Control of Human Movement. Wiley, Hoboken (2009)

    Book  Google Scholar 

Download references

Acknowledgements

This work was made possible by support from the National Key Research and Development Program of China (No. 2017YFB1302300), National Natural Science Foundation of China (NSFC) (No. 6150020696, 61503060), Sichuan Science and Technology Major Projects of New Generation Artificial Intelligence (No. 2018GZDZX0037) and the Fundamental Research Funds for the Central Universities (No. ZYGX2015J148).

Author information

Authors and Affiliations

  1. Center for Robotics, School of Automation and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Guangkui Song, Rui Huang, Jing Qiu & Hong Cheng

  2. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Shuai Fan

Authors
  1. Guangkui Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuai Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guangkui Song.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, G., Huang, R., Qiu, J. et al. Model-based Control with Interaction Predicting for Human-coupled Lower Exoskeleton Systems. J Intell Robot Syst 100, 389–400 (2020). https://doi.org/10.1007/s10846-020-01200-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-020-01200-5

Keywords

Search

Navigation