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Impedance Control with Bounded Actions for Human–Robot Interaction

  • Research Article-Systems Engineering
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Abstract

Human–robot interaction tasks have seen an increased interest in recent years, leading to the need for new proposals both for the design of new robotic systems and for their control and security schemes. In this regard, this work proposes a first approach to impedance control for robot manipulators with bounded inputs which aims to achieve safe human–robot interaction. The proposed scheme has a nonlinear proportional–derivative structure with compensation (PD\(+\)) based on the robot model, makes use of generalized saturation functions to generate bounded control actions, and includes an external torque compensation term based on the user’s electromyographic information. One of the main advantages of this proposal is that the human–robot interaction is defined in the joint space, which avoids singularities, since the robot works within its natural coordinates and the torque applied by the user is estimated at a joint level. The advantage of the novel control scheme can be demonstrated by the stability analysis of the closed-loop system equilibrium point, as well as by comparative analysis of the simulation results.

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References

  1. He, W.; Xue, C.; Yu, X.; Li, Z.; Yang, C.: Admittance-based controller design for physical human–robot interaction in the constrained task space. IEEE Trans. Autom. Sci. Eng. 17(4), 1937–1949 (2020)

    Article  Google Scholar 

  2. Xing, H.; Torabi, A.; Ding, L.; Gao, H.; Deng, Z.; Mushahwar, V.K.; Tavakoli, M.: An admittance-controlled wheeled mobile manipulator for mobility assistance: human–robot interaction estimation and redundancy resolution for enhanced force exertion ability. Mechatronics. (2021). https://doi.org/10.1016/j.mechatronics.2021.102497

    Article  Google Scholar 

  3. Xie, C.; Yang, Q.; Huang, Y.; Su, S.W.; Xu, T.; Song, R.: A Hybrid arm-hand rehabilitation robot with EMG-based admittance controller. IEEE Trans. Biomed. Circuits Syst. (2021). https://doi.org/10.1109/TBCAS.2021.3130090

    Article  Google Scholar 

  4. Taylor, R.H.: A perspective on medical robotics. Proc. IEEE. 94(9), 1652–1664 (2006)

    Article  Google Scholar 

  5. Mendoza, M.; Zavala-Río, A.; Santibàñez, V.; Reyes, F.: Output-feedback proportional-integral-derivative-type control with simple tuning for the global regulation of robot manipulators with input constraints. IET Contr. Theory Appl. 9(14), 2097–2106 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  6. Aguiñaga-Ruiz, E.; Zavala-Río, A.; Santibàñez, V.; Reyes, F.: Global trajectory tracking through static feedback for robot manipulators with bounded inputs. IEEE Trans. Control Syst. Technol. 17(4), 934–944 (2009)

    Article  Google Scholar 

  7. López-Araujo, D.J.; Zavala-Río, A.; Santibàñez, V.; Reyes, F.: A generalized global adaptive tracking control scheme for robot manipulators with bounded inputs. Int. J. Adapt. Control Signal Process. 29(2), 180–200 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Hogan, N.; Krebs, H.I.; Charnnarong, J.; Srikrishna, P.; Sharon, A.: MIT-MANUS: a workstation for manual therapy and training. In: Proceedings of IEEE International Workshop on Robot and Human Communication, pp. 161–165 (1992)

  9. Krebs, H.I.; Volpe, B.T.; Williams, D.; Celestino, J.; Charles, S.K.; Lynch, D.; Hogan, N.: Robot-aided neurorehabilitation: a robot for wrist rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 327–335 (2007)

    Article  Google Scholar 

  10. Nef, T.; Mihelj, M.; Kiefer, G.; Perndl, C.; Muller, R.; Riener, R.: ARMin-Exoskeleton for arm therapy in stroke patients. In: Proceedings of 10th IEEE Int. Conference on Rehabilitation Robotics, pp. 68–74 (2007)

  11. Hogan, N.: Impedance control: an approach to manipulation. ASME J. Dyn. Sys. Meas. Control. 107, 1–24 (1985)

    Article  MATH  Google Scholar 

  12. Hino, M.; Muramatsu, H: Periodic/aperiodic hybrid position/impedance control using periodic/aperiodic separation filter. In: Proceedings of 2021 IEEE International Conference on Mechatronics, pp. 1–6 (2021)

  13. Arnold, J.; Lee, H.: Variable impedance control for pHRI: impact on stability, agility, and human effort in controlling a wearable ankle robot. IEEE Robot. Autom. Lett. 6(2), 2429–2436 (2021)

    Article  Google Scholar 

  14. Zhang, X.; Sun, L.; Kuang, Z.; Tomizuka, M.: Learning variable impedance control via inverse reinforcement learning for force-related tasks. IEEE Robot. Autom. Lett. 6(2), 2225–2232 (2021)

    Article  Google Scholar 

  15. Bonilla, I.; Mendoza, M.; Campos-Delgado, D.U.; Hernández-Alfaro, D.E.: Adaptive impedance control of robot manipulators with parametric uncertainty for constrained path-tracking. Int. J. Appl. Math. Comput. Sci. 28(2), 363–374 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  16. Li, Z.; Liu, J.; Huang, Z.; Peng, Y.; Pu, H.; Ding, L.: Adaptive impedance control of human–robot cooperation using reinforcement learning. IEEE Trans. Ind. Electron. 64(10), 8013–8022 (2017)

    Article  Google Scholar 

  17. Erchao, L.; Zhanming, L.; Junxue, H.: Robotic adaptive impedance control based on visual guidance. Int. J. Smart Sens. Intell. Syst. 8(4), 2159–2174 (2015)

    Google Scholar 

  18. Liu, C.; He, Y.; Chen, X.; Zhang, X.: Discontinuous force-based robot adaptive switching update rate impedance control. In: Proceedings of the IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference, pp. 2573–2580 (2021)

  19. Lakshminarayanan, S.; Kana, S.; Mohan, D.M.; Manyar, O.M.; Then, D.; Campolo, D.: An adaptive framework for robotic polishing based on impedance control. Int. J. Adv. Manuf. Technol. 112(1), 401–417 (2021)

    Article  Google Scholar 

  20. Hu, H.; Wang, X.; Chen, L.: Impedance with finite-time control scheme for robot–environment interaction. Math. Probl. Eng. (2020). https://doi.org/10.1155/2020/2796590

    Article  MathSciNet  Google Scholar 

  21. Lin, G.; Yu, J.; Liu, J.: Adaptive fuzzy finite-time command filtered impedance control for robotic manipulators. IEEE Access. 9, 50917–50925 (2021)

    Article  Google Scholar 

  22. Sun, T.; Peng, L.; Cheng, L.; Hou, Z.G.; Pan, Y.: Stability-guaranteed variable impedance control of robots based on approximate dynamic inversion. IEEE Trans. Syst. Man Cybern. Syst. 51(7), 4193–4200 (2019)

    Article  Google Scholar 

  23. Hamedani, M.H.; Zekri, M.; Sheikholeslam, F.: Adaptive impedance control of uncertain robot manipulators with saturation effect based on dynamic surface technique and self-recurrent wavelet neural networks. Robotica. 37(1), 161–188 (2019)

    Article  Google Scholar 

  24. Arefinia, E.; Talebi, H.A.; Doustmohammadi, A.: A robust adaptive model reference impedance control of a robotic manipulator with actuator saturation. IEEE Trans. Syst. Man Cybern. Syst. 50(2), 409–420 (2017)

    Article  MATH  Google Scholar 

  25. Rodríguez-Liñán, M.; Mendoza, M.; Bonilla, I.; Chávez-Olivares, C.: Saturating stiffness control of robot manipulators with bounded inputs. Int. J. Appl. Math. Comput. Sci. 27(1), 79–90 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  26. Maldonado-Fregoso, B.; Mendoza-Gutierrez, M.; Bonilla-Gutierrez, I.; Vidrios-Serrano, C.: A generalized adaptive stiffness control scheme for robot manipulators with bounded inputs. Asian J. Control. 23(6), 2550–2564 (2021)

    Article  MathSciNet  Google Scholar 

  27. Peng, L.; Hou, Z. G.; Wang, W.: A dynamic EMG-torque model of elbow based on neural networks. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2852–2855 (2015)

  28. Li, Z.; Huang, Z.; He, W.; Su, C.Y.: Adaptive impedance control for an upper limb robotic exoskeleton using biological signals. IEEE Trans. Ind. Electron. 64(2), 1664–1674 (2016)

    Article  Google Scholar 

  29. Khoshdel, V.; Akbarzadeh, A.; Naghavi, N.; Sharifnezhad, A.; Souzanchi-Kashani, M.: sEMG-based impedance control for lower-limb rehabilitation robot. Intell. Serv. Robot. 11(1), 97–108 (2018)

    Article  Google Scholar 

  30. Roveda, L.; Piga, D.: Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks. Auton. Robot. 45(3), 371–388 (2021)

    Article  Google Scholar 

  31. Mendoza, M.; Bonilla, I.; Reyes, F.; González-Galván, E.: A Lyapunov-based design tool of impedance controllers for robot manipulators. Kybernetika. 48(6), 1136–1155 (2012)

    MathSciNet  MATH  Google Scholar 

  32. Han, J.; Ding, Q.; Xiong, A.; Zhao, X.: A state-space EMG model for the estimation of continuous joint movements. IEEE Trans. Ind. Electron. 62(7), 4267–4275 (2015)

    Article  Google Scholar 

  33. Khalil, H.K.: Nonlinear Systems, 3rd edn Prentice Hall, Upper Saddle River (2002)

    MATH  Google Scholar 

  34. Mendoza, M.; Zavala-Río, A.; Santibáñez, V.; Reyes, F.: A generalised PID-type control scheme with simple tuning for the global regulation of robot manipulators with constrained inputs. Int. J. Control. 88(10), 1995–2012 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  35. Vidrios-Serrano, C.; Mendoza, M.; Bonilla, I.; Maldonado-Fregoso, B.: A generalized vision-based stiffness controller for robot manipulators with bounded inputs. Int. J. Control Autom. Syst. 19(1), 548–561 (2021)

    Article  MATH  Google Scholar 

  36. Kelly, R.; Santibáñez, V.; Loría, J.A.: Control of Robot Manipulators in Joint Space. Springer, London (2006)

    Google Scholar 

  37. Ding, Q.; Xiong, A.; Zhao, X.; Han, J.: A novel EMG-driven state space model for the estimation of continuous joint movements. In: Proceeding of the 2011 IEEE International Conference on Systems, Man and Cybernetics, pp. 2891–2897 (2011)

  38. Reyes, F.; Kelly, R.: Experimental evaluation of identification schemes on a direct drive robot. Robotica 15(5), 563–571 (1997)

    Article  Google Scholar 

  39. Wiedemann, L.; Ward, S.; Lim, E.; Wilson, N.; Hogan, A.; Holobar, A.; McDaid, A.: Dataset on isometric contractions of the elbow joint in children with and without spastic Cerebral Palsy: HD-EMG and torque. Mendeley Data, V1 (2019). https://doi.org/10.17632/599rgxhy6m.1

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Acknowledgements

This work was supported by the National Council for Science and Technology, Mexico.

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Víctor I. Ramírez-Vera contributed to conceptualization, formal analysis, investigation, methodology, and writing—original draft, Marco O. Mendoza-Gutiérrez helped in formal analysis, investigation, supervision, and writing—original draft, and Isela Bonilla-Gutiérrez contributed to investigation, methodology, supervision, and writing—review and editing.

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Correspondence to Marco O. Mendoza-Gutiérrez.

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Ramírez-Vera, V.I., Mendoza-Gutiérrez, M.O. & Bonilla-Gutiérrez, I. Impedance Control with Bounded Actions for Human–Robot Interaction. Arab J Sci Eng 47, 14989–15000 (2022). https://doi.org/10.1007/s13369-022-06638-3

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  • DOI: https://doi.org/10.1007/s13369-022-06638-3

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