Skip to main content
SpringerLink
Log in

Optimal Predictive Impedance Control in the Presence of Uncertainty for a Lower Limb Rehabilitation Robot

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

As an innovative concept, an optimal predictive impedance controller (OPIC) is introduced here to control a lower limb rehabilitation robot (LLRR) in the presence of uncertainty. The desired impedance law is considered to propose a conventional model-based impedance controller for the LLRR. However, external disturbances, model imperfection, and parameters uncertainties reduce the performance of the controller in practice. In order to cope with these uncertainties, an optimal predictive compensator is introduced as a solution for a proposed convex optimization problem, which is performed on a forward finite-length horizon. As a result, the LLRR has the desired behavior even in an uncertain environment. The performance and efficiency of the proposed controller are verified by the simulation results.

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. Akdogan E and Adli M A, The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot, Mechatronics, 2011, 21: 509–522.

    Article  Google Scholar 

  2. Christian H, Andy A, Patrick C, et al., Walking and running with passive compliance: Lessons from engineering: A live demonstration of the ATRIAS biped, IEEE Robotics and Automation Magazine, 2018, 25: 23–39.

    Article  Google Scholar 

  3. Calanca A, Muradore R, and Fiorini P, A review of algorithms for compliant control of stiff and fixed-compliance robots, IEEE/ASME Trans. Mechatronics, 2016, 21: 613–624.

    Article  Google Scholar 

  4. Ghajar M H, Keshmiri M, and Bahrami J, Neural-network-based robust hybrid force/position controller for a constrained robot manipulator with uncertainties, Trans. Inst. Meas. Control, 2018, 40: 1625–1636.

    Article  Google Scholar 

  5. Le Flohic J, Paccot F, Bouton N, et al., Application of hybrid force/position control on parallel machine for mechanical test, Mechatronics, 2018, 49: 168–176.

    Article  Google Scholar 

  6. Raibert M H and Craig J J, Hybrid position/force control of manipulators, J. Dyn. Syst. Meas. Control, 1981, 103: 126.

    Article  Google Scholar 

  7. Khoshdel V, Akbarzade A, Naghavi N, et al., sEMG-based impedance control for lower-limb rehabilitation robot, Intelligent Service Robotics, 2018, 11: 97–108.

    Article  Google Scholar 

  8. Towhidkhah F, Gander R E, and Wood H C, Model predictive impedance control: A model for joint movement, J. Mot. Behav., 1997, 29: 209–222.

    Article  Google Scholar 

  9. Souzanchi K M, Arab A, Akbarzadeh T M R, et al., Robust impedance control of uncertain mobile manipulators using time-delay compensation, IEEE Trans. Control Syst. Technol., 2018, 26: 1942–1953.

    Article  Google Scholar 

  10. Hogan N, Impedance control of industrial robots, Comput. Integr. Manuf., 1984, 1: 97–113.

    Article  Google Scholar 

  11. Akdogan E, Aktan M E, Koru A T, et al., Hybrid impedance control of a robot manipulator for wrist and forearm rehabilitation: Performance analysis and clinical results, Mechatronics, 2018, 49: 77–91.

    Article  Google Scholar 

  12. Huang H, Crouch D L, Liu M, et al., A cyber expert system for auto-tuning powered prosthesis impedance control parameters, Ann. Biomed. Eng., 2016, 44: 1613–1624.

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Zhang X, Yue Z, and Wang J, Robotics in lower-limb rehabilitation after stroke, Behavioural Neurology, 2017, 2017: rticle ID 3731802, 13 pages.

    Google Scholar 

  15. Seraji H, Adaptive admittance control: An approach to explicit force control in compliant motion, Proceedings of 1994 IEEE Int. Conf. Robot. Autom., San Diego, CA, USA, 1994.

    Google Scholar 

  16. Khoshdel V and Moeenfard H, Variable impedance control for rehabilitation robot using interval type-2 fuzzy logic, Int. J. Robot., 2015, 4: 46–54.

    Google Scholar 

  17. Fateh M M and Khoshdel V, Voltage-based adaptive impedance force control for a lower-limb rehabilitation robot, Adv. Robot., 2015, 29: 1003–1013.

    Article  Google Scholar 

  18. Jamwal P K, Hussain S, Ghayesh M H, et al., Impedance control of an intrinsically compliant parallel ankle rehabilitation robot, IEEE Trans. Ind. Electron., 2016, 63: 3638–3647.

    Article  Google Scholar 

  19. Kalani H, Moghimi S, and Akbarzadeh A, Towards an SEMG-based tele-operated robot for masticatory rehabilitation, Comput. Biol. Med., 2016, 75: 243–256.

    Article  Google Scholar 

  20. Guo C, Guo S, Ji J, et al., Iterative learning impedance for lower limb rehabilitation robot, J. Healthc. Eng., 2017, 2017: Article ID 6732459, 9 pages.

    Article  Google Scholar 

  21. Li X, Liu Y H, and Yu H, Iterative learning impedance control for rehabilitation robots driven by series elastic actuators, Automatica, 2018, 90: 1–7.

    Article  MathSciNet  Google Scholar 

  22. Karimian M, Towhidkhah F, and Rostami M, Application of model predictive impedance control (MPIC) in analysis of human walking on rough terrains, Int. J. Appl. Electromagn. Mech., 2006, 24: 147–162.

    Article  Google Scholar 

  23. Veneman J F, Kruidhof R, Hekman E E G, et al., Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation, IEEE Trans. Neural Syst. Rehabil. Eng., 2007, 15: 379–386.

    Article  Google Scholar 

  24. Meuleman J, Van Asseldonk E, Van Oort G, et al., LOPES II - Design and evaluation of an admittance controlled gait training robot with shadow-leg approach, IEEE Trans. Neural Syst. Rehabil. Eng., 2016, 24: 352–363.

    Article  Google Scholar 

  25. Van Der Kooij H, Koopman B, and Van Asseldonk E H F, Body weight support by virtual model control of an impedance controlled exoskeleton (LOPES) for gait training, Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, Canada, 2008.

    Google Scholar 

  26. Chen Y, Hu J, Peng L, et al., The FES-assisted control for a lower limb rehabilitation robot: Simulation and experiment, Robot. Biomimetics, 2014, 1: 2.

    Article  Google Scholar 

  27. Gendrin M, Gauthier J, and Lin-Shi X, An impedance control using a finite control set model predictive controller, Proceedings of 2015 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), Valparaiso, Chile, 2015.

    Google Scholar 

  28. Zhakatayev A, Rubagotti M, and Varol H A, Closed-loop control of variable stiffness actuated robots via nonlinear model predictive control, IEEE Access, 2015, 3: 235–248.

    Article  Google Scholar 

  29. Teramae T, Noda T, and Morimoto J, EMG-based model predictive control for physical humanrobot interaction: Application for assist-as-needed control, IEEE Robot. Autom. Lett., 2018, 3: 210–217.

    Article  Google Scholar 

  30. Sharifi I, Doustmohammadi A, and Talebi H A, A safe interaction of robot assisted rehabilitation, based on model-free impedance control with singularity avoidance, IAES Int. J. Robot. Autom., 2015, 4: 98–108.

    Google Scholar 

  31. Lin F J, Chang C K, and Huang P K, FPGA-based adaptive backstepping sliding-mode control for linear induction motor drive, IEEE Trans. Power Electron., 2007, 22: 1222–1231.

    Article  Google Scholar 

  32. Lin F J and Lee C C, Adaptive backstepping control for linear induction motor drive to track periodic references, Electr. Power Appl. IEE Proc., 2000, 147: 449–458.

    Article  Google Scholar 

  33. Lin F J, Chen S G, and Sun I F, Intelligent sliding-mode position control using recurrent wavelet fuzzy neural network for electrical power steering system, Int. J. Fuzzy Syst., 2017, 19: 1344–1361.

    Article  MathSciNet  Google Scholar 

  34. Wu J, Gao J, Song R, et al., The design and control of a 3DOF lower limb rehabilitation robot, Mechatronics, 2016, 33: 13–22.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran

    Mohsen Jalaeian-F., Mohammad Mehdi Fateh & Morteza Rahimiyan

Authors
  1. Mohsen Jalaeian-F.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Mehdi Fateh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Morteza Rahimiyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mohsen Jalaeian-F., Mohammad Mehdi Fateh or Morteza Rahimiyan.

Additional information

This paper was recommended for publication by Editor CHEN Benmei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jalaeian-F., M., Fateh, M.M. & Rahimiyan, M. Optimal Predictive Impedance Control in the Presence of Uncertainty for a Lower Limb Rehabilitation Robot. J Syst Sci Complex 33, 1310–1329 (2020). https://doi.org/10.1007/s11424-020-8335-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-020-8335-5

Keywords

Search

Navigation