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Dynamic characterization and control of a parallel haptic interaction with an admittance type virtual environment

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Abstract

Haptic interfaces, a kinesthetic link between a virtual environment and a human operator play a pivotal role in the reproduction of realistic haptic force feedback of the virtual reality-based simulators. Since most of the practical control theories are model-based, the identification of the robot’s dynamics, for precise modeling of the system dynamics, is a process of high significance and usage. This research addresses dynamic characterization, performance issues, and structural stability, associated with a parallel haptic device interaction with an admittance type virtual environment. In this regard, considering the Lion identification scheme, we characterized the dynamics of a robot nonlinearities in different operational points in the workspace, as a piecewise linear functions. Besides, utilizing the Lyapunov stability theory, we guaranteed the stability of the error dynamic of estimations. Next, by proposing a modified robust model predictive control scheme, one can guarantee the robust output feedback stability for a linear parametric variable system based on a quasi-min–max algorithm, subjected to the input constraints; besides, reducing the unwanted disturbances of the control efforts’ signals caused by switching in the piecewise linear dynamics. The simulations and the experimental implementation of a single-degree-of-freedom virtual-based haptic system are carried out. Results indicate that the identification is of high accuracy, and the appropriate convergence of parameters to the specific values are outlined. Besides, the output tracking error and its derivative behave desirably. The proposed method guarantees the robust stability of the system with output feedback subjected to input constraints. Uncertainties are well addressed in the prediction dynamics, thereby improving the output signals’ convergence to the desired output, being well suited for experimental practices of the haptic system.

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References

  1. Rahm S, Wieser K, Bauer DE, Waibel FW, Meyer DC, Gerber C, Fucentese SF (2018) Efficacy of standardized training on a virtual reality simulator to advance knee and shoulder arthroscopic motor skills. BMC musculoskeletal disorders 19(1):150

    Article  Google Scholar 

  2. Sadeghnejad S, Khadivar F, Abdollahi E, Moradi H, Farahmand F, Sadr Hosseini SM, Vossoughi G (2019) A validation study of a virtual‐based haptic system for endoscopic sinus surgery training. Int J Med Robot Comput Assisted Surg, e2039

  3. Kolbari H, Sadeghnejad S, Parizi AT, Rashidi S, Baltes JH (2016) Extended fuzzy logic controller for uncertain teleoperation system. In: 2016 4th international conference on robotics and mechatronics (ICROM) 2016, pp 78–83, IEEE

  4. Esfandiari M, Sadeghnejad S, Farahmand F, Vosoughi G (2017) Robust nonlinear neural network-based control of a haptic interaction with an admittance type virtual environment. In: 2017 5th RSI international conference on robotics and mechatronics (ICRoM) 2017, pp 322-327. IEEE

  5. Kolbari H, Sadeghnejad S, Bahrami M, Ali KE (2018) Adaptive control of a robot-assisted tele-surgery in interaction with hybrid tissues. J Dyn Syst Meas Contr 140(12):121012

    Article  Google Scholar 

  6. Kolbari H, Sadeghnejad S, Bahrami M, Kamali A (2015) Bilateral adaptive control of a teleoperation system based on the hunt-crossley dynamic model. In: 2015 3rd RSI international conference on robotics and mechatronics (ICROM) 2015, pp 651–656. IEEE

  7. Kolbari H, Sadeghnejad S, Bahrami M, Kamali EA (2015) Nonlinear adaptive control for teleoperation systems transitioning between soft and hard tissues. In: 2015 3rd RSI international conference on robotics and mechatronics (ICROM) 2015, pp 055–060. IEEE

  8. Ebrahimi A, Sadeghnejad S, Vossoughi G, Moradi H, Farahmand F (2016) Nonlinear adaptive impedance control of virtual tool-tissue interaction for use in endoscopic sinus surgery simulation system. In: 2016 4th international conference on robotics and mechatronics (ICROM) 2016, pp 66–71. IEEE

  9. Khadivar F, Sadeghnejad S, Moradi H, Vossoughi G, Farahmand F (2017) Dynamic characterization of a parallel haptic device for application as an actuator in a surgery simulator. In: 2017 5th RSI international conference on robotics and mechatronics (ICRoM) 2017, pp 186–191. IEEE

  10. Torabi A, Khadem M, Zareinia K, Sutherland GR, Tavakoli M (2018) Manipulability of teleoperated surgical robots with application in design of master/slave manipulators. In: 2018 international symposium on medical robotics (ISMR) 2018, pp 1–6. IEEE

  11. Torabi A, Khadem M, Zareinia K, Sutherland GR, Tavakoli M (2019) Application of a redundant haptic interface in enhancing soft-tissue stiffness discrimination. IEEE Robot Autom Lett 4(2):1037–1044

    Article  Google Scholar 

  12. Bazaei A, Chen Z, Yong YK, Moheimani SR (2018) A novel state transformation approach to tracking of piecewise linear trajectories. IEEE Trans Control Syst Technol 26(1):128–138

    Article  Google Scholar 

  13. Li P, Lam J, Kwok K-W, Lu R (2018) Stability and stabilization of periodic piecewise linear systems: a matrix polynomial approach. Automatica 94:1–8

    Article  MathSciNet  Google Scholar 

  14. Haddadi A, Hashtrudi-Zaad K (2012) Real-time identification of Hunt-Crossley dynamic models of contact environments. IEEE Trans Robot 28(3):555–566

    Article  Google Scholar 

  15. Esfandiari M, Sadeghnejad S, Farahmand F, Vosoughi G (2015) Adaptive characterisation of a human hand model during intercations with a telemanipulation system. In: 2015 3rd RSI international conference on robotics and mechatronics (ICROM) 2015, pp 688-693. IEEE

  16. Lion PM (1967) Rapid identification of linear and nonlinear systems. AIAA J 5(10):1835–1842

    Article  ADS  Google Scholar 

  17. Uddin R, Ryu J (2016) Predictive control approaches for bilateral teleoperation. Ann Rev Control 42:82–99

    Article  Google Scholar 

  18. Uddin R, Park S, Park S, Ryu J (2016) Projected predictive Energy-Bounding Approach for multiple degree-of-freedom haptic teleoperation. Int J Control Autom Syst 14(6):1561–1571

    Article  Google Scholar 

  19. Ojaghi P, Bigdeli N, Rahmani M (2016) An LMI approach to robust model predictive control of nonlinear systems with state-dependent uncertainties. J Process Control 47:1–10

    Article  Google Scholar 

  20. Franco E (2016) Combined adaptive and predictive control for a teleoperation system with force disturbance and input delay. Front Robot AI 3:48

    Article  Google Scholar 

  21. Abbas H, Hanema J, Tóth R, Mohammadpour J, Meskin N (2018) An improved robust model predictive control for linear parameter-varying input-output models. Int J Robust Nonlinear Control 28(3):859–880

    Article  MathSciNet  Google Scholar 

  22. Nodozi I, Rahmani M (2017) LMI-based model predictive control for switched nonlinear systems. J Process Control 59:49–58

    Article  Google Scholar 

  23. Yu Y, Luo X, Liu Q (2018) Model predictive control of a dynamic nonlinear PDE system with application to continuous casting. J Process Control 65:41–55

    Article  Google Scholar 

  24. Morsi A, Abbas HS, Mohamed AM (2017) Wind turbine control based on a modified model predictive control scheme for linear parameter-varying systems. IET Control Theory Appl 11(17):3056–3068

    Article  MathSciNet  Google Scholar 

  25. Wan Z, Kothare MV (2002) Robust output feedback model predictive control using off-line linear matrix inequalities. J Process Control 12(7):763–774

    Article  Google Scholar 

  26. Wan Z, Kothare MV (2008) A framework for design of scheduled output feedback model predictive control. J Process Control 18(3):391–398

    Article  Google Scholar 

  27. Park J-H, Kim T-H, Sugie T (2011) Output feedback model predictive control for LPV systems based on quasi-min–max algorithm. Automatica 47(9):2052–2058

    Article  MathSciNet  Google Scholar 

  28. Goodwin GC, Sin KS (2014) Adaptive filtering prediction and control. Courier Corporation, North Chelmsford

    MATH  Google Scholar 

  29. Zhang F (2006) The Schur complement and its applications, vol 4. Springer, Berlin

    Google Scholar 

  30. Lu Y, Arkun Y (2000) Quasi-min-max MPC algorithms for LPV systems. Automatica 36(4):527–540

    Article  MathSciNet  Google Scholar 

  31. Pascal G, Arkadi N, Alan J, Mahmoud C (1995) LMI Control Toolbox, for Use with MATLAB. The Mathworks, Natick

    Google Scholar 

  32. Gahinet P, Nemirovskii A, Laub AJ, Chilali M (1994) The LMI control toolbox. In: Proceedings of the 33rd IEEE conference on 1994 decision and control, 1994, pp 2038–2041. IEEE

  33. Sadeghnejad S, Esfandiari M, Farahmand F, Vossoughi G (2016) Phenomenological contact model characterization and haptic simulation of an endoscopic sinus and skull base surgery virtual system. In: 2016 4th international conference on robotics and mechatronics (ICROM) 2016, pp 84–89. IEEE

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

    Article  Google Scholar 

  35. Sadeghnejad S, Elyasi N, Farahmand F, Vossughi GR, Sadr Hosseini SM (2019) Hyperelastic modeling of sino-nasal tissue for haptic neurosurgery simulation. Sci Iran. https://doi.org/10.24200/sci.2019.50348.1652

    Google Scholar 

  36. Sathya A, Sopasakis P, Van Parys R, Themelis A, Pipeleers G, Patrinos P (2018) Embedded nonlinear model predictive control for obstacle avoidance using PANOC. In: 2018 European control conference (ECC) 2018, pp 1523–1528. IEEE

  37. Kalman RE (1963) Mathematical description of linear dynamical systems. J Soc Ind Appl Math Ser A Control 1(2):152–192

    Article  MathSciNet  Google Scholar 

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Acknowledgements

We are grateful to thank the Djavad Mowafaghian Research Center of Intelligent NeoruRehabilitation Technologies for the support of this research.

Funding

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

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Authors and Affiliations

  1. School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

    Farshad Khadivar, Hamed Moradi & Gholamreza Vossoughi

  2. Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Soroush Sadeghnejad

Authors
  1. Farshad Khadivar
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  2. Soroush Sadeghnejad
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  3. Hamed Moradi
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  4. Gholamreza Vossoughi
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Correspondence to Hamed Moradi.

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

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