Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

This article presents a novel adaptive iterative learning control (AILC), and designs a human-in-loop control pattern (HIL-CP), which simulates the proposed approach using different lower limb rehabilitation robot models. The stability of the AILC controller is proposed and verified via a Lyapunov-like function, where novel controller shows strong robustness in disturbances environment. Based on AILC, the core of the HIL-CP interactive control mode is to estimate the human surface electromyography by neural network model and get the real-time desired trajectory to iterate out the optimal actual tracking trajectory, which reduce the tracking error quickly and ensure the rehabilitation training effect of patients. Furthermore, the MATLAB software is employed to conduct simulation experiments the proposed approach. The simulation results show that the HIL-CP is highly efficient and rapidly convergent in a satisfied degree. The angle error is \({\mathrm{{0.25}}^\text {o}}\pm {\mathrm{{0.2}}^\text {o}} \) for patients and \({\mathrm{{0.03}}^\text {o}}\pm {\mathrm{{0.02}}^\text {o}} \) for healthy people. Compared with the existing sliding mode controller, it is proven that the AILC controller is much more effective and noise-tolerant ability in the presence of bounded nonlinear disturbance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ai, Q. S., Ke, D., Zuo, J., et al. (2019). High-order model-free adaptive iterative learning control of pneumatic artificial muscle with enhanced convergence. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2952810.

    Article  Google Scholar 

  • Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of robotics by learning. Journal of Robotic System, 1(2), 123–140.

    Article  Google Scholar 

  • Choi, W., Won, J., Lee, J., et al. (2017). Low stiffness design and hysteresis compensation torque control of SEA for active exercise rehabilitation robots. Autonomous Robots, 41(5), 1221–1242.

    Article  Google Scholar 

  • Freeman, C. T. (2014). Newton-method based iterative learning control for robot-assisted rehabilitation using FES. Mechatronics, 24(8), 934–943.

    Article  Google Scholar 

  • Freeman, T. C. (2015). Upper limb electrical stimulation using input–output linearization and iterative learning control. IEEE Transactions on Control Systems Technology, 23(4), 1546–1554.

    Article  Google Scholar 

  • Geravand, M., Korondi, P. Z., Werner, C., et al. (2017). Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance. Autonomous Robots, 41(3), 575–592.

    Article  Google Scholar 

  • Gross, H. M., Scheidig, A., Debes, K., et al. (2017). ROREAS: Robot coach for walking and orientation training in clinical post-stroke rehabilitationprototype implementation and evaluation in field trials. Autonomous Robots, 41(3), 679–698.

    Article  Google Scholar 

  • Hesse, S., Schmidt, H., Werner, C., & Bardeleben, A. (2003). Upper and lower extremity robotic devices for rehabilitation and for studying motor control. Current Opinion in Neurology, 16(6), 705–710.

    Article  Google Scholar 

  • Jamwal, P. K., Xie, S. Q., & Hussain, S. (2014). An adaptive wearable parallel robot for the treatment of ankle injuries. IEEE/ASME Transactions on Mechatronics, 19(1), 64–75.

    Article  Google Scholar 

  • Jarrett, C., & Mcdaid, A. (2017). Robust control of a cable-driven soft exoskeleton joint for intrinsic human-robot interaction. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(7), 976–986.

    Article  Google Scholar 

  • Jin, L., Huang, Z. G., Li, Y. H., Sun, Z. B., Li, H. W., & Zhang, J. L. (2019). On modified multi-output Chebyshev-polynomial feed-forward neural network for pattern classification of wine regions. IEEE Access, 7, 1973–1980.

    Article  Google Scholar 

  • Jin, L., Li, S., & Hu, B. (2018). RNN models for dynamic matrix inversion: A control-theoretical perspective. IEEE Transactions on Industrial Informatics, 14, 189–199.

    Article  Google Scholar 

  • Jin, L., Li, S., Xiao, L., Lu, R. B., & Liao, B. L. (2018). Cooperative motion generation in a distributed network of redundant robot manipulators with noises. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48, 1715–1724.

    Article  Google Scholar 

  • Jin, L., Zhang, Y., Li, S., & Zhang, Y. (2016). Modified ZNN for time-varying quadratic programming with inherent tolerance to noises and its application to kinematic redundancy resolution of robot manipulators. IEEE Transactions on Industrial Electronics, 63, 6978–6988.

    Article  Google Scholar 

  • Kawamoto, H., & Sankair, Y. (2012). Power assist method based on phase sequence and muscle force condition for HAL. Advanced Robotics, 219(7), 717–734.

    Article  Google Scholar 

  • Langhorne, P., Coupar, F., & Pollock, A. (2009). Motor recovery after stroke: A systematic review. The Lancet Neurology, 8(8), 714–754.

    Article  Google Scholar 

  • Li, X., Liu, Y. H., & Yu, H. (2018). Iterative learning impedance control for rehabilitation robots driven by series elastic actuators. Automatica, 90, 1–7.

    Article  MathSciNet  Google Scholar 

  • Li, X., Pan, Y., Chen, G., & Yu, H. (2017). Multi-modal control scheme for rehabilitation robotic exoskeletons. The International Journal of Robotics Research, 36(5–7), 759–777.

    Article  Google Scholar 

  • Li, X., Pan, Y., Gong, C., et al. (2017). Adaptive human-robot interaction control for robots driven by series elastic actuators. IEEE Transactions on Robotics, 33(1), 169–182.

    Article  Google Scholar 

  • Lu, R., Li, Z., Su, C. Y., et al. (2014). Development and learning control of a human limb with a rehabilitation exoskeleton. IEEE Transactions on Industrial Electronics, 61(7), 3776–3785.

    Article  Google Scholar 

  • Meng, W., Liu, Q., Zhou, Z. D., et al. (2015). Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics, 31, 132–145.

    Article  Google Scholar 

  • Mushage, B. O., Chedjou, J. C., & Kyamakya, K. (2017). Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation. Nonlinear Dynamics, 87(3), 2021–2037.

    Article  Google Scholar 

  • Neumann, D. A. (2010). Kinesiology of the Musculoskeletal System: Foundations for Rehabilitation. Amsterdam: Mosby Elsevier.

    Google Scholar 

  • Pratt, J. E., Krupp, B. T., Morse, C. J., & Collins, S. H. (2004). The roboKnee: an exoskeleton for enhancing strength and endurance during walking.

  • Quintero, D., Villarreal, D. J., Lambert, D. J., et al. (2018). Continuous-phase control of a powered kneecankle prosthesis: amputee experiments across speeds and inclines. IEEE Transactions on Robotics, 34(3), 686–701.

    Article  Google Scholar 

  • Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., & Dietz, V. (2005). Patient-cooperative strategies for robot-aided treadmill training: first experimental results. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(3), 380C394

  • Roy, A., Krebs, H. I., Williams, D. J., et al. (2009). Robot-aided neurorehabilitation: A novel robot for ankle rehabilitation. IEEE Transactions on Robotics, 25(3), 569–582.

    Article  Google Scholar 

  • Shen, P., Zhang, X., & Fang, Y. (2018). Complete and time-optimal path-constrained trajectory planning with torque and velocity constraints: Theory and applications. IEEE/ASME Transactions on Mechatronics, 23(2), 735–746.

    Article  Google Scholar 

  • Sun, Z. B., Duan, X. Q., Li, F., et al., (2019). RBF Neural Network-sliding model control approach for lower limb rehabilitation robot based on gait trajectories of sEMG estimation. International Conference on Intelligent Control and Information Processing. Marrakesh, Morocco, December 14–19

  • Sun, Z. B., Li, F., Zhang, B. C., et al. (2019). Different modified zeroing neural dynamics with inherent tolerance to noises for time-varying reciprocal problems: a control-theoretic approach. Neurocomputing, 337(14), 165–179.

    Article  Google Scholar 

  • Sun, Z. B., Tian, Y. T., Li, H. Y., & Wang, J. (2016). A superlinear convergence feasible sequential quadratic programming algorithm for bipedal dynamic walking robot via discrete mechanics and optimal control. Optimal Control Applications and Methods, 37(6), 1139–1161.

    Article  MathSciNet  Google Scholar 

  • Sun, Z. B., Tian, Y. T., & Wang, J. (2018). A novel projected Fletcher-Reeves conjugate gradient approach for finite-time optimal robust controller of linear constraints optimization problem: Application to bipedal walking robots. Optimal Control Applications and Methods, 39(1), 130–159.

    Article  MathSciNet  Google Scholar 

  • Tsukahara, A., Hasegawa, Y., & Sankai, Y. (2011). Gait support for complete spinal cord injury patient by synchronized leg-swing with HAL. IEEE/RSJ International Conference on Intelligent Robots Systems (pp. 1737–1742).

  • Vecchio, D. D., Marino, R., & Tomei, P. (2004). Adaptive iterative learning control for robot manipulators. Automatica, 40, 1195–1203.

    Article  MathSciNet  Google Scholar 

  • WHO. (2020). Online.

  • Wu, J. P., & Gao, J. W. (2016). The design and control of a 3DOF lower limb rehabilitation robot. Mechatronics, 33, 13–22.

    Article  Google Scholar 

  • Zhang, X., Chen, X., Farzadpour, F., & Fang, Y. (2018). A visual distance approach for multi-camera deployment with coverage optimization. IEEE/ASME Transactions on Mechatronics, 23(3), 1007–1018.

    Article  Google Scholar 

  • Zhang, J. J., Fiers, P., Witte, K. A., et al. (2017). Human-in-the-loop optimization of exoskeleton assistance during walking. Science, 356(6344), 1280–1284.

    Article  Google Scholar 

  • Zhu, X. F., & Wang, J. H. (2018). Double iterative compensation learning control for active training of upper limb rehabilitation robot. International Journal of Control, Automation and Systems, 16, 1–11.

    Article  Google Scholar 

  • Zoss, A. B., Kazerooni, H., & Chu, A. (2006). Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Transactions on Mechatronics, 222(8), 128–138.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers and the Technical Editor for their valuable comments and suggestions on revising this paper.

Author information

Authors and Affiliations

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

    Zhongbo Sun

  2. Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun, 130025, China

    Zhongbo Sun

  3. Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Feng Li

  4. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China

    Feng Li

  5. Department of Control Engineering, Changchun University of Technology, Changchun, 130012, China

    Yufeng Lian, Shuaishi Liu & Keping Liu

  6. The Second Hospital Norman Bethune of Jilin University, Changchun, 130000, China

    Xiaoqin Duan

  7. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Long Jin

Authors
  1. Zhongbo Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoqin Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Long Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yufeng Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shuaishi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Keping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Keping Liu.

Additional information

Publisher's Note

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

The work is supported in part by the National Natural Science Foundation of China under Grant Nos. 61873304, 11701209 and 51875047, and also in part by the China Postdoctoral Science Foundation Funded Project under Grant Nos. 2018M641784, 2019T120240 and also in part by the Key Science and Technology Projects of Jilin Province, China, Grant Nos.20200201291JC,and also in part by the Fundamental Research Funds for the Central Universities (No. lzujbky-2019-89)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Z., Li, F., Duan, X. et al. A novel adaptive iterative learning control approach and human-in-the-loop control pattern for lower limb rehabilitation robot in disturbances environment. Auton Robot 45, 595–610 (2021). https://doi.org/10.1007/s10514-021-09988-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-021-09988-3

Keywords

Search

Navigation