Journal of Mechanical Science and Technology

, Volume 32, Issue 2, pp 835–843 | Cite as

Robust teleoperation in a non-visible environment with a new prediction scheme



We propose a new prediction scheme for robust teleoperation in a non-visible environment. The positioning error caused by the time delay in a non-visible environment was compensated for by the Smith predictor, and the sensory data was estimated by the grey model. The Smith predictor was effective in compensating for the positioning error caused by the time delay with a precise system model. Therefore, a dynamic model of a mobile robot was derived in this research. To minimize the unstable and erroneous states caused by the time delay, the estimated sensor data were sent to the operator. Through simulations, the possibility of compensating the errors caused by the time delay was verified using the Smith predictor. In addition, the estimation reliability of the measurement data has been demonstrated. Robust teleoperations in a non-visible environment have been performed with a mobile robot to avoid obstacles and move to the target position by the proposed prediction scheme, which combines the Smith predictor with the grey model. Although a human operator is involved in the teleoperation loop, the compensation effects have been demonstrated.


Grey prediction Smith prediction Time delay Mobile robot 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Y. Yokokohji, T. Imaida and T. Yoshikawa, Bilateral control with energy balance monitoring under time-varying communication delay, Proceedings of ICRA'00 (IEEE International Conference on Robotics and Automation), 3 (2000) 2684–2689.Google Scholar
  2. [2]
    H. Arioui et al., Master-model based time-delayed force feedback interaction: Experimental results, Proceedings of the 2002 IEEE International Symposium on Intelligent Control (2002) 896–901.Google Scholar
  3. [3]
    H.-J. Choi and S. Jung, Design of a time-delay compensator using neural network in a tele-operation system, Journal of Korean Institute of Intelligent Systems, 21 (4) (2011) 449–455.CrossRefGoogle Scholar
  4. [4]
    C. Seo et al., Robustly stable bilateral teleoperation under time-varying delays and data losses: An energy-bounding approach, J. of Mechanical Science and Technology, 5 (8) (2011) 2089–2100.CrossRefGoogle Scholar
  5. [5]
    R. C. Miall et al., Is the cerebellum a smith predictor?, J. of Motor Behavior, 25 (3) (1993) 203–216.CrossRefGoogle Scholar
  6. [6]
    A. C. Smith and H.-Z. Keyvan, Smith predictor type control architectures for time delayed teleoperation, International J. of Robotics Research, 25 (8) (2006) 797–818.CrossRefGoogle Scholar
  7. [7]
    C. C. Hang, K. W. Lim and B. W. Chong, A dual-rate adaptive digital Smith predictor, Automatica, 25 (1) (1989) 1–16.MathSciNetCrossRefMATHGoogle Scholar
  8. [8]
    D. Q. Truong, K. K. Ahn and N. T. Trung, Design of an advanced time delay measurement and a smart adaptive unequal interval grey predictor for real-time nonlinear control systems, IEEE Transactions on Industrial Electronics, 60 (10) (2013) 4574–4589.CrossRefGoogle Scholar
  9. [9]
    J. S. Lee, S. M. Seong and K. H. Kang, Sliding mode control design for passenger car ABS considering grey model prediction, The Korean Society of Automotive Engineers (2013) 99–105.Google Scholar
  10. [10]
    D. K. Han and P.-H. Chang, Robust tracking of robot manipulator with nonlinear friction using time delay control with gradient estimator, J. of Mechanical Science and Technology, 24 (8) (2010) 1743–1752.CrossRefGoogle Scholar
  11. [11]
    T. Fukao, H. Nakagawa and N. Adachi, Adaptive tracking control of a nonholonomic mobile robot, IEEE Transactions on Robotics and Automation, 16 (5) (2000) 609–615.CrossRefGoogle Scholar
  12. [12]
    P. Boscariol et al., A delayed force-reflecting haptic controller for master–slave neurosurgical robots, Advanced Robotics, 29 (2) (2015) 127–138.CrossRefGoogle Scholar
  13. [13]
    M. R. Matausek and A. D. Micic, A modified Smith predictor for controlling a process with an integrator and long dead-time, IEEE Transactions on Automatic Control, 41 (8) (1996) 1199–1203.MathSciNetCrossRefMATHGoogle Scholar
  14. [14]
    K. J. Astrom, C. H. Chang and B. C. Lim, A new Smith predictor for controlling a process with an integrator and long dead-time, IEEE Transactions on Automatic Control, 39 (2) (1994) 343–345.MathSciNetCrossRefMATHGoogle Scholar
  15. [15]
    S. Majhi and D. P. Atherton, Obtaining controller parameters for a new Smith predictor using autotuning, Automatica, 36 (11) (2000) 1651–1658.MathSciNetCrossRefMATHGoogle Scholar
  16. [16]
    S. Majhi and D. P. Atherton, Modified Smith predictor and controller for processes with time delay, IEEE Proceedings-Control Theory and Applications, 146 (5) (1999) 359–366.CrossRefGoogle Scholar
  17. [17]
    Y.-F. Wang, Predicting stock price using fuzzy grey prediction system, Expert Systems with Applications, 22 (1) (2002) 33–38.CrossRefGoogle Scholar
  18. [18]
    S.-J. Huang and C.-L. Huang, Control of an inverted pendulum using grey prediction model, IEEE Transactions on Industry Applications, 36 (2) (2000) 452–458.MathSciNetCrossRefGoogle Scholar
  19. [19]
    Y.-P. Huang and C.-H. Huang, Real-valued genetic algorithms for fuzzy grey prediction system, Fuzzy Sets and Systems, 87 (3) (1997) 265–276.CrossRefGoogle Scholar
  20. [20]
    D.-H. Lee et al., Tactile navigation system using a haptic device, J. of Institute of Control, Robotics and Systems, 20 (8) (2014) 807–814 (in Korean).CrossRefGoogle Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics EngineeringPusan National UniversityBusanKorea

Personalised recommendations