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Journal of Intelligent & Robotic Systems

, Volume 60, Issue 1, pp 83–110 | Cite as

A Hybrid Control Approach for Non-invasive Medical Robotic Systems

  • Swandito Susanto
  • Sunita Chauhan
Article

Abstract

In this paper, a hybrid supervisory control approach adopted for a non-invasive medical robot called Focused Ultrasound Surgical Robot—Breast Surgery (FUSBOT-BS) is elaborated. The system was built for the use in the breast surgery with high intensity focused ultrasound (HIFU) as the means of the treatment. A number of different control strategies such as PID and model-based control were incorporated into a family of controllers to create the hybrid control. Depending on the objective, the supervisory control determines the type of controller used for the specified task so as to maximize the advantages of each of the controllers. Before it was implemented into the actual robotic system the then proposed control approach was modeled and simulated using Matlab®. This control approach was developed based on a review of popular control approaches used in medical robotic systems, in order to look at the feasibility of having a uniform control strategy for a spectrum of medical robotic system. With unified control strategy it is possible to have a safety standard regulation for the medical robotic systems which is currently difficult to be done because of various control strategies adopted by each of the medical robotic systems.

Keywords

Hybrid control Medical robotic systems Non-invasive surgery High intensity focused ultrasound 

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References

  1. 1.
    Camarillo, D.B., Krummel, T.M., Salisbury, J.K. Jr.: Robotic technology in surgery: past, present, and future. Am. J. Surg. 188, 2–15 (2009). doi: 10.1016/j.amjsurg.2004.08.025 CrossRefGoogle Scholar
  2. 2.
    Taylor, R.H., Stoianovici, D.: Medical robotics in computer-integrated surgery. IEEE Trans. Robot. Autom. 19(5), 765–781 (2003). doi: 10.1109/TRA.2003.817058 CrossRefGoogle Scholar
  3. 3.
    Cleary, K., Nguyen, C.: State of the art in surgical robotics: clinical applications and technology challenges. Comput. Aided Surg. 6(6), 312–340 (2001)CrossRefGoogle Scholar
  4. 4.
    Swandito, G.G.N.: Kumar, Chauhan, S.: Control hierarchy of medical robotic systems for non-invasive for surgery. In: Proceedings of the 12th International Conference on Biomedical Engineering, Singapore (2005)Google Scholar
  5. 5.
    Mishra, R.K., Chauhan, S.: Safety of surgical robots: a fundamental aspect. In: Proceedings of the 12th ISMCR—Towards Advanced Robot Systems and Virtual Reality, Bourges, France (2002)Google Scholar
  6. 6.
    Kazanzides, P., Zuhars, J., Mittelstadt, B., Taylor, R.H.: Force sensing and control for a surgical robot. In: Proceedings of the IEEE International Conference on Robotics and Auto, Nice, France, pp. 612–617 (1992)Google Scholar
  7. 7.
    Kennedy, C.W., Desai, J.P.: Model-based control of the Mitsubishi PA-10 robot arm: application to robot-assisted surgery. In: Proceedings of the IEEE International Conference on Robotics and Auto, New Orleans, LA, pp. 2523–2528 (2004)Google Scholar
  8. 8.
    Zemiti, N., Ortmaier, T., Morel, G.: A new robot for force control in minimally invasive surgery. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Sendai, Japan, pp. 3643–3648 (2004)Google Scholar
  9. 9.
    Gu, J.J., Meng, M., Cook, A., Faulkner, M.G., Liu, P.X.: Sensing and control of robotic prosthetic eye for ocular implant. In: Proceedings of the 26th IEEE International Conference on Intelligent Robots and Systems, Hawaii, USA, pp. 2166–2171 (2001)Google Scholar
  10. 10.
    Ginhoux, R., Ganglouf, J., Mathelin, M., Soler, L., Sanchez, M.M.A., Marescaux, J.: Active filtering of physiological motion in robotized surgery using predictive control. IEEE Trans. Robot. 21(1), 67–79 (2005). doi: 10.1109/TRO.2004.833812 CrossRefGoogle Scholar
  11. 11.
    Zhu, W.H., Salcudean, S.E., Bachmann, S., Abolmaesumi, P.: Motion/force/image control of a diagnostic ultrasound robot. In: Proceedings IEEE International Conference on Robotics and Auto, San Francisco, CA, pp. 1580–1585 (2000)Google Scholar
  12. 12.
    Ang, K.H., Chong, G., Yun, L.: PID control systems analysis, design, technology. IEEE Trans. Control Syst. Technol. 13(4), 559–576 (2005). doi: 10.1109/TCST.2005.847331 CrossRefGoogle Scholar
  13. 13.
    Mishra, R.K.: FUSBOT-BS: Technical Manual. Robotics Research Centre, Nanyang Technological University, Singapore (2004)Google Scholar
  14. 14.
    Chauhan, S.: A HIFU medical robotic system for organotripsy and tissue ablation – the FUSBOT. In: Proceedings International Conference on Computing, Communication, and Control Technologies, Austin (Texas), USA (2004)Google Scholar
  15. 15.
    Whitcomb, L.L., Arimoto, S., Naniwa, T., Ozaki, F.: Adaptive model-based hybrid control of geometrically constrained robot arms. IEEE Trans. Robot Autom. 13, 105–116 (1997)CrossRefGoogle Scholar
  16. 16.
    Mills, J.K.: Hybrid control: A constrained motion perspective. J. Robot. Syst. 8(2), 135–158 (1991). doi: 10.1002/rob.4620080202 CrossRefMATHGoogle Scholar
  17. 17.
    Koutsoukos, X.D., Antsaklis, P.J., Stiver, J.A., Lemmon, M.D.: Supervisory control of hybrid systems. Proc. IEEE 88(7), 1026–1049 (2000). doi: 10.1109/5.871307 CrossRefGoogle Scholar
  18. 18.
    Morse, A.S.: Control Using Logic-Based Switching Trends in Control: A European Perspective, pp. 69–113. Springer, London (1995)Google Scholar
  19. 19.
    Enste, U., Epple, U.: Hybrid structure in process control. In: Proceedings of the American Control Conference, San Diego, California, pp. 4482–4485 (1999)Google Scholar
  20. 20.
    Astrom, K.J., Hagglund, T., Hang, C.C., Ho, W.K.: Automatic tuning and adaptation for PID controllers—a survey. Control Eng. Pract. 1(4), 699–714 (1993). doi: 10.1016/0967–0661(93)91394-C CrossRefGoogle Scholar
  21. 21.
    Shigemasa, T., Yukitomo, M., Kuwata, R. A model driven PID Control system and its case studies. In: Proceedings of the IEEE International Conference on Control Application, Glasgow, UK, pp. 571–576 (2002)Google Scholar
  22. 22.
    Garcia, C.E., Carelli, R., Postigo, J.F., Soria, C.: Supervisory control for a telerobotic system: a hybrid control approach. Control Eng. Pract. 11, 805–817 (2003). doi: 10.1016/S0967-0661(02)00206-X CrossRefGoogle Scholar
  23. 23.
    Cao, C.W.: Supervisory control of a class of hybrid dynamic systems. IEEE 22, 967–970 (1993)Google Scholar
  24. 24.
    Khalil, W., Kleinfinger, J.F.: A new geometric notation for open and closed-loop robots. In: Proceedings IEEE Conference on Robotics and Automation, San Francisco, CA, pp. 1174–1179 (1986)Google Scholar
  25. 25.
    Schilling, R.J.: Fundamentals of Robotics: Analysis and Control. Prentice-Hall, Singapore (1990)Google Scholar
  26. 26.
    Kozlowski, K.: Modelling and Identification in Robotics: Advances in Industrial Control. Springer, Great Britain (1998)Google Scholar
  27. 27.
    Schiavicco, L., Siciliano, B.: Modelling and Control of Robot Manipulators. Prentice-Hall, Singapore (2000)Google Scholar
  28. 28.
    Olsson, H., Astrom, K.J., De Wit, C.C., Gafvert, M., Lischinsky, P.: Friction models and friction compensation. Eur. J. Control. 4(3), 176–195 (1998)MATHGoogle Scholar
  29. 29.
    Dupont, P., Armstrong, B., De Wit, C.C.: A survey of models, analysis tools and compensation methods for the control of machines with friction. Int. J. Autom. 30(7), 1083–1138 (1994). doi: 10.1016/0005-1098(94)90209-7 MATHGoogle Scholar
  30. 30.
    De Wit, C.C., Lischinsky, P.: Adaptive friction compensation with partially known dynamic friction model. Int. J. Adapt Control Signal Process. 11, 65–80 (1997). doi: 10.1002/(SICI)1099-1115(199702)11:1<65::AID-ACS395>3.0.CO;2-3 CrossRefMATHGoogle Scholar
  31. 31.
    The MathWorks Inc: US. MATLAB. http://www.mathworks.com (2007)
  32. 32.
    Gu, D.W., Petkov, P.H.R., Konstantinov, M.M.: Robust Control Design With MATLAB. Springer, London (2006)Google Scholar
  33. 33.
    Intellicam System: IntelliPIX. http://www.cctvdealers.com/ (2007)
  34. 34.
    Qu, Z.H.: Robust Control of Nonlinear Uncertain System. Wiley Series in Nonlinear Science. Wiley-Interscience, USA (1998)Google Scholar
  35. 35.
    Cadic, M.: Strongly robust adaptive control: the strong robustness approach. Dissertation in partial fulfillment of the requirements of the Dutch Institute of Systems and Control (DISC) for graduate study, Twente University Press, The Netherlands (2003)Google Scholar
  36. 36.
    Shinners, S.M.: Advance Modern Control System Theory and Design. Wiley-Interscience, USA (1998)Google Scholar
  37. 37.
    Galil Motion Controller, U.S.A.: Manuals and command references. http://www.galilmc.com/ (2005)
  38. 38.
    Mahfouf, M., Abbod, M.F., Linkens, D.A.: A survey of fuzzy logic monitoring and control utilisation in medicine. Artif. Intell. Med. 21, 27–42 (2001). doi: 10.1016/S0933-3657(00)00072-5 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore

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