Enhance Transparency of Force Feedback Interaction Series Mechanism by SMC Strategy

  • Zhi Hu
  • Yueying Wang
  • Guohua CuiEmail author
  • Dan Zhang


To enhance the fidelity of virtual surgery, the force feedback interaction mechanism would simulate the force information in real surgery. This paper first introduces evaluation methods of fidelity, then it explores evaluation methods of force feedback fidelity, and put up the evaluation methods and indexes of force feedback fidelity. In rigid force feedback interaction mechanism, fidelity is mainly shown as transparency. It means the transfer function of the mechanism needs approach to 1, while friction, gravity, slip of the linear drive and other factors would affect transparency of the mechanism. This study introduces a 5-DOF (degree of freedom) force feedback series mechanism with long stroke. It discusses gravity compensation and simulation of typical virtual environment. It uses sliding mode control (SMC) to eliminate the effect of parameter variety, inaccurate modeling and other factors to improve system robustness. And it adds disturbance observer to the controller to eliminate gravity inaccuracy. Finally, it evaluates the enhancement of force feedback fidelity.


Fidelity force feedback sliding mode control transparency virtual surgery 


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  1. [1]
    C. Z. Zhou, L. Xie, X. L. Shen, M. S. Luo, Z. L. Wu, and L. X. Gu, “Cardiovascular–interventional–surgery virtual training platform and its preliminary evaluation,” The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 11, no. 3, pp. 375–387, Oct. 2014.Google Scholar
  2. [2]
    P. V. Suryawanshi and D. N. N. Mhala, “A simulation method of soft tissue cutting in virtual environment with haptics,” Journal of Engineering Research and Applications, vol. 5, no. 7, pp. 63–66, Jan. 2015.Google Scholar
  3. [3] Scholar
  4. [4]
    R. M. Satava, “Medical virtual reality: The current status of the future,” Studies in Health Technology & Informatics, vol. 29, no. 29, pp. 100–106, 1996.Google Scholar
  5. [5]
    G. B. Chung, S. M. Kim, and S. G. Lee, “An image–guided robotic surgery system for spinal fusion,” International Journal of Control, Automation, and Systems, vol. 4, no. 1, pp. 30–41, Feb. 2006.Google Scholar
  6. [6]
    D. B. Kaber, Y. J. Li, M. Clamann, and Y. S. Lee, “Investigating human performance in a virtual reality haptic simulator as influenced by fidelity and system latency,” IEEE Transactions on Systems Man & Cybernetics Part A Systems & Humans, vol. 42, no. 6, pp. 1562–1566, Nov. 2012.Google Scholar
  7. [7]
    M. Mahvash and V. Hayward, “Haptic rendering of cutting: a fracture mechanics approach,” Haptics–e, vol. 2, no. 3, pp. 1–12, Jan. 2001.Google Scholar
  8. [8]
    Z. Hu and P. Cai, “Robust force feedback control of 5–DOF haptic device,” Proc. of the 33rd Chinese Control Conference, IEEE, pp. 4417–4422, 2014.Google Scholar
  9. [9]
    R. Weller, D. Mainzer, G. Zachmann, M. Sagardia, T. Hulin, and C. Preusche, “A benchmarking suite for 6–DOF real time collision response algorithms,” Acm Symposium on Virtual Reality Software & Technology, ACM, pp. 63–70, 2010.Google Scholar
  10. [10]
    M. Sagardia, T. Hulin, C. Preusche, and G. Hirzinger, “A benchmark of force quality in haptic rendering,” International Human Computer Interaction (HCI), 2009.Google Scholar
  11. [11]
    T. Massie and K. Salisbury, “The PHANTOM haptic interface: a device for probing virtual objects,” Proceedings of ASME WAM, vol. 55, no. 1, pp. 295–300, Jan. 1994.Google Scholar
  12. [12]
    D Wang, J Xiao, and Y Zhang, “Evaluation of haptic rendering methods,” Springer Berlin Heidelberg, pp. 117–130, 2014.Google Scholar
  13. [13]
    C. Luciano, P. Banerjee, and T. Defanti, “Haptics–based virtual reality periodontal training simulator,” Springer Virtual Reality, vol. 13, no. 2, pp. 69–85, June 2009.Google Scholar
  14. [14]
    I. Kruglikova, T. P. Grantcharov, A. M. Drewes, and P. Funchjensen, “Assessment of early learning curves among nurses and physicians using a high–fidelity virtual–reality colonoscopy simulator,” Surgical Endoscopy, vol. 24, no. 2, pp. 366–70, Feb. 2009.Google Scholar
  15. [15]
    J. F. Yu and Y. Kui, “Development of a virtual surgery system with a virtual scalpel,” IEEE International Conference on Information Acquisition, IEEE, 2005.Google Scholar
  16. [16]
    B. Wu, Studies on Interaction Force Acquistion and Control Technology in Thoracoscopic Virtual Surgery, Ph.D. dissertation, Dept. Elect. Info. Elect. Eng, Shanghai Jiao Tong University, Shanghai, China, 2014.Google Scholar
  17. [17]
    S. Feyzabadi, S. Straube, M. Folgheraiter, E. A. Kirchner, and S. K. Kim, “Human force discrimination during active arm motion for force feedback design,” IEEE Transactions on Haptics, vol. 6, no. 3, pp. 309–319, Jul. 2013.Google Scholar
  18. [18]
    X. D. Pang, H. Z. Tan, and N. I. Durlach, “Manual discrimination of force using active finger motion,” Perception & Psychophysics, vol. 49, no. 6, pp. 531–540, Nov. 1991.Google Scholar
  19. [19]
    F. Barbagli, K. Salisbury, C. Ho, and H. Z. Tan, “Haptic discrimination of force direction and the influence of visual information,” ACM Transactions on Applied Perception, vol. 3, no. 2, pp. 125–135, Apr. 2006.Google Scholar
  20. [20]
    M. Bouzit, G. Burdea, G. Popescu, and R. Boian, “The rutgers master II–New design force–feedback glove,” IEEE/ASME Transactions on Mechatronics, vol. 7, no. 2, pp. 256–263, Jun. 2002.Google Scholar
  21. [21]
    C. D. Lee and D. A. Lawrence, “Isotropic force control for haptic interfaces,” Control Engineering Practice, vol. 12, no. 11, pp. 1423–1436, Dec. 2004.Google Scholar
  22. [22]
    F. Ferguene and R. Toum, “Dynamic external force feedback loop control of a robot manipulator using a neural compensator–application to the trajectory following in an unknown environment,” International Journal of Applied Mathematics and Computer Science, vol. 19, no. 1, pp. 113–126, Mar. 2009.zbMATHGoogle Scholar
  23. [23]
    D. Kushida, “Flexible motion realized by force–free control,” International Conference on Robotics & Automation, vol. 3, pp. 2747–2752, Dec. 2001.Google Scholar
  24. [24]
    J. J. Jiang, Y. R. Zhang, and D. X. Wang, “Stall torque control and update rate problem in force feedback system,” Microcomputer Information, vol. 21, no. 8, pp. 93–95, Sep. 2005.Google Scholar
  25. [25]
    R. M. Murray, Z. Li, and S. S. Sastry, A Mathematical Introduction to Robotic Manipulation, CRC Press, 2017.zbMATHGoogle Scholar
  26. [26]
    Y. L. Wei, J. B. Qiu, and H. K. Lam, “A novel approach to reliable output feedback control of fuzzy–affine systems with time delays and sensor faults,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1808–1823, Dec. 2017.Google Scholar
  27. [27]
    Y. L. Wei, J. B. Qiu, H. R. Karimi, and W. Q. Ji, “A novel memory filtering design for semi–Markovian jump timedelay systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 12, pp. 2229–2241, Oct. 2017.Google Scholar
  28. [28]
    X. H. Chang and Y. M. Wang, “Peak–to–peak filtering for networked nonlinear DC motor systems with quantization,” IEEE Transactions on Industrial Informatics, vol. 14, no. 12, pp. 5378–5388, Feb. 2018.Google Scholar
  29. [29]
    X. H. Chang, Z. M. Li, and J. H. Park, “Fuzzy generalized H2 filtering for nonlinear discrete–time systems with measurement quantization,” IEEE Transactions on Systems Man and Cyberneti–cs: Systems, vol. 48, no. 12, pp. 2419–2430, Sep. 2017.Google Scholar
  30. [30]
    Y. F. Yin, G. D. Zong, and X. D. Zhao, “Improved stability criteria for switched positive linear systems with average dwell time switching,” Journal of the Franklin Institute, vol. 354, no. 8, pp. 3472–3484, May 2017.MathSciNetzbMATHGoogle Scholar
  31. [31]
    Y. F. Yin, X. D. Zhao, and X. L. Zheng, “New stability and stabilization conditions of switched systems with modedependent average dwell time,” Circuits, Systems, and Signal Processing, vol. 36, no. 1, pp. 82–98, Jan. 2017.MathSciNetzbMATHGoogle Scholar
  32. [32]
    Y. L. Wei, J. H. Park, J. B. Qiu, L. G. Wu, and H. Y. Jung, “Sliding mode control for semiMarkovian jump systems via output feedback,” Automatica, vol. 81, pp. 133–141, Jul. 2017.MathSciNetzbMATHGoogle Scholar
  33. [33]
    Y. Wang, H. R. Karimi, H. K. Lam, and H. Shen, “An improved result on exponential stabilization of sampled–data fuzzy systems,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3875–3883, July 2018.Google Scholar
  34. [34]
    Y. Wang, H. R. Karimi, H. Shen, Z. Fang, and M. Liu, “Fuzzy–model–based sliding mode control of nonlinear descriptor systems,” IEEE Transactions on Cybernetics, pp. 1–11, June 2018.Google Scholar
  35. [35]
    Y. Wang, P. Shi, and H. Yan, “Reliable control of fuzzy singularly perturbed systems and its application to electronic circuits,” IEEE Transactions on Circuits and Systems I Regular Papers, vol. 65, no. 10, pp. 3519–3528, Jun. 2018.MathSciNetGoogle Scholar
  36. [36]
    Y. Wang, Y. Xia, H. Shen, and P. Zhou, “SMC design for robust stabilization of nonlinear Markovian jump singular systems,” IEEE Transactions on Automatic Control, vol. 63, no. 1, pp. 219–224, Jun. 2018.MathSciNetzbMATHGoogle Scholar
  37. [37]
    Y. Wang, H. Shen, H. R. Karimi, and D. Duan, “Dissipativity–based fuzzy integral sliding mode control of continuous–time T–S fuzzy systems,” IEEE Transactions Fuzzy Systems, vol. 26, no. 3, pp. 1164–1176, Jun. 2017.Google Scholar
  38. [38]
    J. Yang, S. H. Li, and X. H. Yu, “Sliding–mode control for systems with mismatched uncertainties via a disturbance observer,” IEEE Transactions on Industry Electronics, vol. 60, no. 1, pp. 3988–3993, Jan. 2013.Google Scholar
  39. [39]
    D. Zehar, K. Benmahammed, and K. Behih, “Control for underactuated systems using sliding mode observer,” nternational Journal of Control, Automation, and Systems, vol. 16, no. 2, pp. 739–748, Apr. 2018.Google Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Intelligent Robotics Research Center and Laboratory of Intelligent Control and RoboticsShanghai University of Engineering ScienceShanghaiP. R. China
  2. 2.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina

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