Sensor-Less Bilateral Teleoperation System Based on Non Linear Inverse Modelling with Signal Prediction

  • Mateusz SakówEmail author
  • Arkadiusz Parus
  • Mirosław Pajor
  • Karol Miądlicki
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 934)


In the paper a sensor-less control scheme for a bilateral teleoperation system with a force-feedback based on a prediction of an input and an output of a non-linear inverse model by prediction blocks was presented. As a part of the paper a method of a time constant estimation of the prediction block was also presented. The prediction method of an input and an output of an inverse model was designed to minimize the effect of the transport delay and the phase shift of sensors, actuators and mechanical objects. The solution is an alternative to complex non-linear models like artificial neural networks, which requires complex stability analysis and control systems with a high computing power. The effectiveness of the method has been verified on the hydraulic manipulator test stand.


Force-feedback Teleoperation Non linear inverse modeling Delay 



The work was carried out as part of the PBS3/A6/28/2015 project, “The use of augmented reality, interactive voice systems and operator interface to control a crane”, financed by NCBiR.


  1. 1.
    Miądlicki, K., Pajor, M.: Overview of user interfaces used in load lifting devices. Int. J. Sci. Eng. Res. 6(9), 1215–1220 (2015)Google Scholar
  2. 2.
    Ferrell, W.R.: Delayed force feedback. Hum. Fact.: J. Hum. Fact. Ergon. Soc. 8(5), 449–455 (1966)CrossRefGoogle Scholar
  3. 3.
    Sheridan, T.B., Ferrell, W.R.: Human control of remote computer-manipulators. In: Proceedings of the 1st International Joint Conference on Artificial Intelligence, pp. 483–494. Morgan Kaufmann Publishers Inc., Washington, DC (1969)Google Scholar
  4. 4.
    Moreau, R., et al.: Sliding-mode bilateral teleoperation control design for master–slave pneumatic servo systems. Control Eng. Pract. 20(6), 584–597 (2012)CrossRefGoogle Scholar
  5. 5.
    Chang, M.-K.: An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators. Control Eng. Pract. 18(1), 13–22 (2010)CrossRefGoogle Scholar
  6. 6.
    Polushin, I.G., Takhmar, A., Patel, R.V.: Projection-based force-reflection algorithms with frequency separation for bilateral teleoperation. IEEE/ASME Trans. Mechatron. 20(1), 143–154 (2015)CrossRefGoogle Scholar
  7. 7.
    Hulin, T., Albu-Schäffer, A., Hirzinger, G.: Passivity and stability boundaries for haptic systems with time delay. IEEE Trans. Control Syst. Technol. 22(4), 1297–1309 (2014)CrossRefGoogle Scholar
  8. 8.
    Tadano, K., Kawashima, K.: Development of 4-DOFs forceps with force sensing using pneumatic servo system. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, Orlando, FL, USA (2006)Google Scholar
  9. 9.
    Zhai, D.H., Xia, Y.: Adaptive control of semi-autonomous teleoperation system with asymmetric time-varying delays and input uncertainties. IEEE Trans. Cybern. 47, 3621–3633 (2017)CrossRefGoogle Scholar
  10. 10.
    Saków, M., Pajor, M., Parus, A.: Estimation of environmental forces impact on remote control system with force-feedback and upper limb kinematics. Modelowanie Inzynierskie 58, 113–122 (2016). (in Polish)Google Scholar
  11. 11.
    Saków, M., Parus, A.: Sensorless control scheme for teleoperation with force-feedback, based on a hydraulic servo-mechanism, theory and experiment. Meas. Autom. Monit. 62(12), 417–425 (2016)Google Scholar
  12. 12.
    Saków, M., Miądlicki, K., Parus, A.: Self-sensing teleoperation system based on 1-DoF pneumatic manipulator. J. Autom. Mob. Robot. Intell. Syst. 11(1), 64–76 (2017)Google Scholar
  13. 13.
    Zhou, M., Ben-Tzvi, P.: RML glove - an exoskeleton glove mechanism with haptics feedback. IEEE/ASME Trans. Mechatron. 20(2), 641–652 (2015)CrossRefGoogle Scholar
  14. 14.
    Miądlicki, K., Pajor, M.: Real-time gesture control of a CNC machine tool with the use Microsoft Kinect sensor. Int. J. Sci. Eng. Res. 6(9), 538–543 (2015)Google Scholar
  15. 15.
    Pajor, M., Miądlicki, K., Saków, M.: Kinect sensor implementation in FANUC robot manipulation. Arch. Mech. Technol. Autom. 34(3), 35–44 (2014)Google Scholar
  16. 16.
    Stuart, K.D., Majewski, M., Trelis, A.B.: Intelligent semantic-based system for corpus analysis through hybrid probabilistic neural networks. In: International Symposium on Neural Networks. Springer, Heidelberg (2011)Google Scholar
  17. 17.
    Stuart, K.D., Majewski, M.: Intelligent opinion mining and sentiment analysis using artificial neural networks. In: International Conference on Neural Information Processing. Springer, Istanbul (2015)Google Scholar
  18. 18.
    Kim, W.S., Hannaford, B., Fejczy, A.K.: Force-reflection and shared compliant control in operating telemanipulators with time delay. IEEE Trans. Robot. Autom. 8(2), 176–185 (1992)CrossRefGoogle Scholar
  19. 19.
    Hyun Chul, C., et al.: Sliding-mode-based impedance controller for bilateral teleoperation under varying time-delay. In: IEEE International Conference on Robotics and Automation, Proceedings 2001 ICRA, Seoul, South Korea (2001)Google Scholar
  20. 20.
    Ge, X., et al.: Analysis of a model-free predictor for delay compensation in networked systems. In: Time Delay Systems, pp. 201–215. Springer, Heidelberg (2017)Google Scholar
  21. 21.
    Kaya, I.: Obtaining controller parameters for a new PI-PD Smith predictor using autotuning. J. Process Control 13(5), 465–472 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mateusz Saków
    • 1
    Email author
  • Arkadiusz Parus
    • 1
  • Mirosław Pajor
    • 1
  • Karol Miądlicki
    • 1
  1. 1.Institute of Mechanical TechnologyWest Pomeranian University of TechnologySzczecinPoland

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