Skip to main content

Advertisement

SpringerLink
Log in

Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

Haptic training simulators generally consist of three major components, namely a human operator, a haptic interface, and a virtual environment. Appropriate dynamic modeling of each of these components can have far-reaching implications for the whole system's performance improvement in terms of transparency, the analogy to the real environment, and stability. In this paper, we developed a virtual-based haptic training simulator for Endoscopic Sinus Surgery by doing a dynamic characterization of the phenomenological sinus tissue fracture in the virtual environment, using an input-constrained linear parametric variable model. A parallel robot manipulator equipped with a calibrated force sensor is employed as a haptic interface. A lumped five-parameter single-degree-of-freedom mass-stiffness-damping impedance model is assigned to the operator’s arm dynamic. A robust online output feedback quasi-min–max model predictive control framework is proposed to stabilize the system during the switching between the piecewise linear dynamics of the virtual environment. The simulations and the experimental results demonstrate the effectiveness of the proposed control algorithm in terms of robustness and convergence to the desired impedance quantities.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Amirkhani, G., Farahmand, F., Yazdian, S.M., Mirbagheri, A.: An extended algorithm for autonomous grasping of soft tissues during robotic surgery. Int. J. Med. Robot. Comput. Assist. Surg. 16(5), 1–15 (2020). https://doi.org/10.1002/rcs.2122

    Article  Google Scholar 

  2. Bowthorpe, M., Tavakoli, M.: Generalized predictive control of a surgical robot for beating-heart surgery under delayed and slowly-sampled ultrasound image data. IEEE Robot. Autom. Lett. 1(2), 892–899 (2016). https://doi.org/10.1109/LRA.2016.2530859

    Article  Google Scholar 

  3. Choi, K.S., He, X., Chiang, V.C.L., Deng, Z.: A virtual reality based simulator for learning nasogastric tube placement. Comput. Biol. Med. 57, 103–115 (2015)

    Article  Google Scholar 

  4. Esfandiari, M., Farahmand, F.: Emg-based neural network model of human arm dynamics in a haptic training simulator of sinus endoscopy. IEEE Int. Conf. Robot. Autom. (2021). https://doi.org/10.1109/ICRA48506.2021.9561555

    Article  Google Scholar 

  5. Esfandiari, M., Sadeghnejad, S., Farahmand, F., Vosoughi, G.: Robust nonlinear neural network-based control of a haptic interaction with an admittance type virtual environment. IEEE 5th RSI Int. Conf. Robot. Mechat. (ICROM), pp. 322–327. (2017). 1109/ICRoM.2017.8466196

  6. Esfandiari, M., Sadeghnejad, S., Farahmand, F., Vosoughi, G.: Adaptive characterisation of a human hand model during intercations with a telemanipulation system. IEEE 3rd RSI Int. Conf. Robot. Mechatron. (ICROM), pp. 688–693. (2015). 1109/ICRoM.2015.7367866

  7. Faulwasser, T., Findeisen, R.: Nonlinear model predictive control for constrained output path following. IEEE Trans. Automat. Contr. 61(4), 1026–1039 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Golnary, F., Moradi, H.: Dynamic modelling and design of various robust sliding mode controls for the wind turbine with estimation of wind speed. Appl. Math. Model. 65, 566–585 (2019). https://doi.org/10.1016/j.apm.2018.08.030

    Article  MathSciNet  MATH  Google Scholar 

  9. Hannaford, B., Ryu, J.H.: Time-domain passivity control of haptic interfaces. IEEE Trans. Robot. Autom. 18(1), 1–10 (2002). https://doi.org/10.1109/70.988969

    Article  Google Scholar 

  10. Harischandra, P.A., Abeykoon, A.M.: Upper-limb tele-rehabilitation system with force sensorless dynamic gravity compensation. Int. J. Soc. Robot. 11(4), 621–630 (2019). https://doi.org/10.1007/s12369-019-00522-1

    Article  Google Scholar 

  11. Hokayem, P.F., Spong, M.W.: Bilateral teleoperation: an historical survey. Automatica 42(12), 2035–2057 (2006). https://doi.org/10.1016/j.automatica.2006.06.027

    Article  MathSciNet  MATH  Google Scholar 

  12. Jain, S., Lee, S., Barber, S.R., Chang, E.H., Son, Y.J.: Virtual reality based hybrid simulation for functional endoscopic sinus surgery. IISE Trans. Healthc. Syst. Eng. 10(2), 127–141 (2020). https://doi.org/10.1080/24725579.2019.1692263

    Article  Google Scholar 

  13. Ji, Y., Gong, Y.: Adaptive control for dual-master/single-slave nonlinear teleoperation systems with time-varying communication delays. IEEE Trans. Instrum. Meas. 70, 1–15 (2021). https://doi.org/10.1109/TIM.2021.3075527

    Article  Google Scholar 

  14. Khadivar, F., Sadeghnejad, S., Moradi, H., Vossoughi, G.: Dynamic characterization and control of a parallel haptic interaction with an admittance type virtual environment. Meccanica 55(3), 435–452 (2020). https://doi.org/10.1007/s11012-020-01125-1

    Article  MathSciNet  MATH  Google Scholar 

  15. Khadivar, F., Sadeghnejad, S., Moradi, H., Vossoughi, G., Farahmand, F.: Dynamic characterization of a parallel haptic device for application as an actuator in a surgery simulator. IEEE 5th RSI Int. Conf. Robot. Mechat. (ICROM), pp. 186–191. (2017). https://doi.org/10.1109/ICRoM.2017.8466168

  16. Kolbari, H., Sadeghnejad, S., Bahrami, M., Ali, K.E.: Adaptive control of a robot-assisted tele-surgery in interaction with hybrid tissues. J. Dyn. Syst Meas Control (2018). https://doi.org/10.1115/1.4040818

    Article  Google Scholar 

  17. Kolbari, H., Sadeghnejad, S., Bahrami, M., Kamali, E.A.: Nonlinear adaptive control for teleoperation systems transitioning between soft and hard tissues. IEEE 3rd RSI Int. Conf. Robot. Mechat. (ICROM), pp. 055–060. (2015). 1109/ICRoM.2015.7367760

  18. Kolbari, H., Sadeghnejad, S., Bahrami, M., Kamali, A.: Bilateral adaptive control of a teleoperation system based on the hunt-crossley dynamic model. IEEE 3rd RSI Int. Conf. Robot. Mechat. (ICROM), pp. 651–656 (2015). 1109/ICRoM.2015.7367860

  19. Lee, S.M., Kwon, O.M., Park, J.H.: Output feedback model predictive tracking control using a slope bounded nonlinear model. J. Optim. Theory Appl. 160, 239–254 (2014). https://doi.org/10.1007/s10957-012-0201-8

    Article  MathSciNet  MATH  Google Scholar 

  20. Lee, S.M., Won, S.C., Park, J.H.: New robust model predictive control for uncertain systems with input constraints using relaxation matrices. J. Optim. Theory Appl. 138, 221–234 (2008). https://doi.org/10.1007/s10957-008-9375-5

    Article  MathSciNet  MATH  Google Scholar 

  21. Li, H., Zhang, L., Kawashima, K.: Operator dynamics for stability condition in haptic and teleoperation system: a survey. Int. J. Med. Robot. Comput. Assist. Surg. 14(2), e1881 (2018). https://doi.org/10.1002/rcs.1881

    Article  Google Scholar 

  22. Lu, Y., Arkun, Y.: Quasi-min–max MPC algorithms for LPV systems. Automatica 36(4), 527–540 (2000). https://doi.org/10.1016/S0005-1098(99)00176-4

    Article  MathSciNet  MATH  Google Scholar 

  23. Moreira, P., Zemiti, N., Liu, C., Poignet, P.: Viscoelastic model based force control for soft tissue interaction and its application in physiological motion compensation. Comput. Meth. Programs Biomed. 116(2), 52–67 (2014). https://doi.org/10.1016/j.cmpb.2014.01.017

    Article  Google Scholar 

  24. Norizuki, H., Uchimura, Y.: Contact prediction control for a teleoperation system with time delay. IEEJ J. Ind. Appl. 7(1), 102–108 (2018). https://doi.org/10.1541/ieejjia.7.102

    Article  Google Scholar 

  25. Park, J.H., Kim, T.H., Sugie, T.: Output feedback model predictive control for LPV systems based on quasi-min–max algorithm. Automatica 47(9), 2052–2058 (2011). https://doi.org/10.1016/j.automatica.2011.06.015

    Article  MathSciNet  MATH  Google Scholar 

  26. Piromchai, P.: Virtual reality surgical training in ear, nose and throat surgery. Int. J. Clin. Med. 5(10), 558–566 (2014). https://doi.org/10.4236/ijcm.2014.510077

    Article  Google Scholar 

  27. Polushin, I.G., Liu, P.X., Lung, C.H.: A force-reflection algorithm for improved transparency in bilateral teleoperation with communication delay. IEEE/ASME Trans. Mechatron. 12(3), 361–374 (2007). https://doi.org/10.1109/TMECH.2007.897285

    Article  Google Scholar 

  28. Rosseau, G., Bailes, J., del Maestro, R., Cabral, A., Choudhury, N., Comas, O., DiRaddo, R.: The development of a virtual simulator for training neurosurgeons to perform and perfect endoscopic endonasal transsphenoidal surgery. Neurosurgery 73(suppl_1), S85–S93 (2013). https://doi.org/10.1227/NEU.0000000000000112

    Article  Google Scholar 

  29. De Rossi, G., Muradore, R.: A bilateral teleoperation architecture using Smith predictor and adaptive network buffering. IFAC-PapersOnLine 50(1), 11421–11426 (2017)

    Article  Google Scholar 

  30. Sadeghnejad, S., Elyasi, N., Farahmand, F., Vossughi, G., Sadr Hosseini, S.M.: Hyperelastic modeling of sino-nasal tissue for haptic neurosurgery simulation. Sci. Iran. 27(3), 1266–1276 (2020)

    Google Scholar 

  31. Sadeghnejad, S., Esfandiari, M., Farahmand, F., Vossoughi, G.: Phenomenological contact model characterization and haptic simulation of an endoscopic sinus and skull base surgery virtual system. IEEE 4th Int. Conf. Robot. Mechatron. (ICROM), pp. 84–89 (2016). https://doi.org/10.1109/ICRoM.2016.7886822

  32. Sadeghnejad, S., Farahmand, F., Vossoughi, G., Moradi, H., Hosseini, S.M.S.: Phenomenological tissue fracture modeling for an endoscopic sinus and skull base surgery training system based on experimental data. Med. Eng. Phys. 68, 85–93 (2019)

    Article  Google Scholar 

  33. Sadeghnejad, S., Khadivar, F., Abdollahi, E., Moradi, H., Farahmand, F., Sadr Hosseini, S.M., Vossoughi, G.: A validation study of a virtual-based haptic system for endoscopic sinus surgery training. Int. J. Med. Robot. Comput. Assist. Surg. 15(6), e2039 (2019). https://doi.org/10.1002/rcs.2039

    Article  Google Scholar 

  34. Sapkaroski, D., Baird, M., McInerney, J., Dimmock, M.R.: The implementation of a haptic feedback virtual reality simulation clinic with dynamic patient interaction and communication for medical imaging students. J. Med. Radiat. Sci. 65(3), 218–225 (2018). https://doi.org/10.1002/jmrs.288

    Article  Google Scholar 

  35. Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. Int. J. Adv. Manuf. Technol. 117(5), 1327–1349 (2021). https://doi.org/10.1007/s00170-021-07682-3

    Article  Google Scholar 

  36. Seo, C., Kim, J.P., Kim, J., Ahn, H.S., Ryu, J.: Robustly stable bilateral teleoperation under time-varying delays and data losses: an energy-bounding approach. J. Mech. Sci. Technol. 25(8), 2089–2100 (2011). https://doi.org/10.1007/s12206-011-0523-8

    Article  Google Scholar 

  37. Sirouspour, S., Shahdi, A.: Model predictive control for transparent teleoperation under communication time delay. IEEE Trans. Robot. 22(6), 1131–1145 (2006)

    Article  Google Scholar 

  38. Song, A., Wu, C., Ni, D., Li, H., Qin, H.: One-therapist to three-patient telerehabilitation robot system for the upper limb after stroke. Int. J. Soc. Robot. 8(2), 319–329 (2016). https://doi.org/10.1007/s12369-016-0343-1

    Article  Google Scholar 

  39. Sun, D., Naghdy, F., Du, H.: Application of wave-variable control to bilateral teleoperation systems: a survey. Annu. Rev. Control. 38(1), 12–31 (2014). https://doi.org/10.1016/j.arcontrol.2014.03.002

    Article  Google Scholar 

  40. Tavakoli, M., Carriere, J., Torabi, A.: Robotics, smart wearable technologies, and autonomous intelligent systems for healthcare during the COVID-19 pandemic: an analysis of the state of the art and future vision. Adv. Intell. Syst. 2(7), 2000071 (2020). https://doi.org/10.1002/aisy.202000071

    Article  Google Scholar 

  41. Torabi, A., Zareinia, K., Sutherland, G.R., Tavakoli, M.: Dynamic reconfiguration of redundant haptic interfaces for rendering soft and hard contacts. IEEE Trans. Haptics 13(4), 668–678 (2020). https://doi.org/10.1109/TOH.2020.2988495

    Article  Google Scholar 

  42. Uddin, R., Ryu, J.: Predictive control approaches for bilateral teleoperation. Annu. Rev. Control. 42, 82–99 (2016). https://doi.org/10.1016/j.arcontrol.2016.09.003

    Article  Google Scholar 

  43. Vrooijink, G.J., Denasi, A., Grandjean, J.G., Misra, S.: Model predictive control of a robotically actuated delivery sheath for beating heart compensation. Int. J. Robot. Res. 36(2), 193–209 (2017). https://doi.org/10.1177/0278364917691113

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Djavad Mowafaghian Research Center for Intelligent NeoruRehabilitation Technologies of the Sharif University of Technology and the Bio-Inspired System Design Laboratory of the Biomedical Engineering Department at Amirkabir University of Technology (Tehran Polytechnic) for supporting this research. In addition, we are grateful to Ehsan Abdollahi for helping us to prepare the experiment set-up system.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Biomedical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), No. 350, Hafez Ave, Valiasr Square, Tehran, 1591634311, Islamic Republic of Iran

    Soroush Sadeghnejad

  2. School of Mechanical Engineering, Sharif University of Technology, Azadi Ave., Tehran, 1458889694, Islamic Republic of Iran

    Farshad Khadivar, Mojtaba Esfandiari, Golchehr Amirkhani, Hamed Moradi, Farzam Farahmand & Gholamreza Vossoughi

Authors
  1. Soroush Sadeghnejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farshad Khadivar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mojtaba Esfandiari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Golchehr Amirkhani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hamed Moradi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Farzam Farahmand
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gholamreza Vossoughi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soroush Sadeghnejad.

Ethics declarations

Conflict of interest

The authors confirm that this manuscript and its corresponding research work involve no conflict of interest.

Ethical Approval

Not required.

Additional information

Publisher's Note

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

Communicated by Lorenz Biegler.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sadeghnejad, S., Khadivar, F., Esfandiari, M. et al. Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System. J Optim Theory Appl 198, 745–766 (2023). https://doi.org/10.1007/s10957-023-02241-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10957-023-02241-0

Keywords

Search

Navigation