Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-19T19:39:50.414Z Has data issue: false hasContentIssue false
  • English
  • Français

Performance improvement of bilateral control systems using derivative of force

Published online by Cambridge University Press:  30 July 2018

Eray A. Baran ,
Tarik Uzunovic  and
Asif Sabanovic
Eray A. Baran*
Affiliation:
Faculty of Engineering and Natural Sciences, Istanbul Bilgi University, Istanbul, Turkey.
Tarik Uzunovic
Affiliation:
Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina. E-mail: tuzunovic@etf.unsa.ba
Asif Sabanovic
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey. E-mail: asif@sabanciuniv.edu
*
*Corresponding author. E-mail: eray.baran@bilgi.edu.tr
Get access Rights & Permissions [Opens in a new window]

Summary

This paper proposes a bilateral control structure with a realization of the force derivative in the control loop. Due to the inherent noisy nature of the force signal, most teleoperation schemes can make use of only a proportional (P) control structure in the force channel of the bilateral controllers. In the proposed scheme, an α–β–γ filter is designed to smoothly differentiate the force signal obtained from a reaction force observer integrated to both of the master and slave plants. The differentiated force signal is then used in a proportional-derivative (PD) force controller working together with a disturbance observer. In order to design the overall bilateral controller, an environment model based on pure spring structure is assumed. The controller is designed to enforce an exponentially decaying tracking error for both position and force signals. With the presented controller design approach, one can independently tune the controller gains of the force and the position control channels. The proposed approach is experimentally tested in a platform consisted of direct drive linear motors. As illustrated by the experiment results, the contribution of the PD control in the force channel improves the teleoperation performance especially under hard-contact motion scenarios by attenuating the oscillations, hence, improving the transparency when compared to the structures using only a P force control.

Keywords

HapticsBilateral controlTeleoperationPD force controlα–β–γ filterHard contact motion control
Type
Articles
Information
Robotica , Volume 36 , Issue 11 , November 2018 , pp. 1627 - 1640
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Hace, A. and Franc, M., “Pseudo-sensorless high-performance bilateral teleoperation by sliding-mode control and FPGA,” IEEE/ASME Trans. Mechatronics 19 (1), 384–393 (2014).Google Scholar
2. Mitsantisuk, C., Ohishi, K. and Katsura, S., “Estimation of action/reaction forces for the bilateral control using Kalman filter,” IEEE Trans. Ind. Electron. 59 (11), 4383–4393 (2012).Google Scholar
3. Mizoguchi, T., Nozaki, T. and Ohnishi, K., “Stiffness transmission of scaling bilateral control system by gyrator element integration,” IEEE Trans. Ind. Electron. 61 (2), 1033–1043 (2014).Google Scholar
4. Sakaino, S., Sato, T. and Ohnishi, K., “Multi-DOF micro-macro bilateral controller using oblique coordinate control,” IEEE Trans. Ind. Electron. 7 (3), 446–454 (2011).Google Scholar
5. Katsura, S., Yamanouchi, W. and Yokokura, Y., “Real-world haptics: Reproduction of human motion,” IEEE Ind. Electron. Mag. 6 (1), 25–31 (2012).Google Scholar
6. Wilde, M., Chua, Z. K. and Fleischner, A., “Effects of multivantage point systems on the teleoperation of spacecraft docking,” IEEE Trans. Human-Mach. Syst. 44 (2), 200–210 (2014).Google Scholar
7. Stoll, E., Letschnik, J., Walter, U., Artigas, J., Kremer, P., Preusche, C. and Hirzinger, G., “On-orbit servicing,” IEEE Robot. Autom. Mag. 16 (4), 29–33 (2009).Google Scholar
8. Saltaren, R., Aracil, R., Alvarez, C., Yime, E. and Sabater, J. M., “Field and service applications-exploring deep sea by teleoperated robot-an underwater parallel robot with high navigation capabilities,” IEEE Robot. Autom. Mag. 14 (3), 65–75 (2007).Google Scholar
9. Ferraguti, F., Preda, N., Manurung, A., Bonfe, M., Lambercy, O., Gassert, R., Muradore, R., Fiorini, P. and Secchi, C., “An energy tank-based interactive control architecture for autonomous and teleoperated robotic surgery,” IEEE Trans. Robot. 31 (5), 1073–1088 (2015).Google Scholar
10. Burgner, J., Rucker, D. C., Gilbert, H. B., Swaney, P. J., Russell, P. T., Weaver, K. D. and Webster, R. J., “A telerobotic system for transnasal surgery,” IEEE/ASME Trans. Mechatron. 19 (3), 996–1006 (2014).Google Scholar
11. Tanaka, H., Ohnishi, K., Nishi, H., Kawai, T., Morikawa, Y., Ozawa, S. and Furukawa, T., “Implementation of bilateral control system based on acceleration control using Fpga for multi-DOF haptic endoscopic surgery robot,” IEEE Trans. Ind. Electron. 56 (3), 618–627 (2009).Google Scholar
12. Lawrence, D. A., “Stability and transparency in bilateral teleoperation,” IEEE Trans. Robot. Autom. 9 (5), 624–637 (1993).Google Scholar
13. Miyazaki, F., Matsubayash, S., Yoshimi, T. and Arimoto, S., “A New Control Methodology Toward Advanced Teleoperation of Master-Slave Robot Systems,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3 (1986) pp. 997–1002.Google Scholar
14. Hannaford, B. and Fiorini, P., “A Detailed Model of Bi-Lateral Teleoperation,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (1988)Google Scholar
15. Raju, G. J., Verghese, G. C. and Sheridan, T. B., “Design Issues In 2-Port Network Models of Bilateral Remote Manipulation,” Proceedings of the IEEE International Conference on Robotics and Automation (1989) pp. 1316–1321.Google Scholar
16. Niemeyer, G. and Slotine, J. J. E., “Stable adaptive teleoperation,” IEEE J. Ocean. Eng. 16 (1), 152–162 (1991).Google Scholar
17. Natori, K., Tsuji, T., Ohnishi, K., Hace, A. and Jezernik, K., “Time delay compensation by communication disturbance observer for bilateral teleoperation under time-varying delay,” IEEE Trans. Ind. Electron. 57 (3), 1050–1062 (2010).Google Scholar
18. Sankaranarayanan, G. and Hannaford, B., “Experimental Comparison of Internet Haptic Collaboration with Time-Delay Compensation Techniques,” Proceedings of the IEEE International Conference on Robotics and Automation (2008) pp. 206–211.Google Scholar
19. Ghavifekr, A. A., Ghiasi, A. R. and Badamchizadeh, M. A., “Discrete-time control of bilateral teleoperation systems: A review,” Robotica 36 (4), 552–569 (2018).Google Scholar
20. Nozaki, T., Mizoguchi, T., Saito, Y., Nakano, T. and Ohnishi, K., “Bilateral control method based on transformation matrix relating motion features and tool coordinates,” IEEJ J. Ind. Appl. 2 (1), 67–73 (2013).Google Scholar
21. Yajima, S. and Katsura, S., “Decoupled bilateral control based on modal space observer in master-slave systems with different masses,” Electr. Eng. Japan 193 (1), 10–20 (2015).Google Scholar
22. Ogawa, K., Kozuki, R. and Ohnishi, K., “A Method for Improving Scaling Bilateral Control by Integration of Physical and Control Scaling Ratio,” Proceedings of the IEEE International Conference on Mechatronics (2015) pp. 516–521.Google Scholar
23. Poorten, E. V., Kanno, T. and Yokokohji, Y., “Robust Variable-Scale Bilateral Control for Micro Teleoperation” Proceedings of the IEEE International Conference on Robotics and Automation (2008) pp. 655–662.Google Scholar
24. Onal, C. D. and Sitti, M., “Teleoperated 3-D force feedback from the nanoscale with an atomic force microscope,” IEEE Trans. Nanotechnol. 9 (1), 46–54 (2010).Google Scholar
25. Salcudean, S. E., Ku, S. and Bell, G., “Performance Measurement in Scaled Teleoperation for Microsurgery,” CVRMed-MRCAS'97, Springer (1997) pp. 789–798.Google Scholar
26. Sakaino, S., Sato, T. and Ohnishi, K., “Oblique Coordinate Control for Advanced Motion Control-Applied to Micro-Macro Bilateral Control,” Proceedings of the IEEE International Conference on Mechatronics (2009) pp. 1–6.Google Scholar
27. Nozaki, T., Mizoguchi, T. and Ohnishi, K., “Decoupling strategy for position and force control based on modal space disturbance observer,” IEEE Trans. Ind. Electron. 61 (2), 1022–1032 (2014).Google Scholar
28. Rebelo, J., Sednaoui, T., den Exter, E. B., Krueger, T. and Schiele, A., “Bilateral robot teleoperation: A wearable arm exoskeleton featuring an intuitive user interface,” IEEE Robot. Autom. Mag. 4 (21), 62–69 (2014).Google Scholar
29. Kesner, S. B. and Howe, R. D., “Robotic catheter cardiac ablation combining ultrasound guidance and force control,” Int. J. Robot. Res. 33 (4), 631–644 (2014).Google Scholar
30. Yang, B., Roys, S., Tan, U. X., Philip, M., Richard, H., Gullapalli, R. P. and Desai, J. P., “Design, development and evaluation of a master slave surgical system for breast biopsy under continuous MRI,” Int. J. Robot. Res. 33 (4), 616–630 (2014).Google Scholar
31. Suzumura, A. and Fujimoto, Y., “Generalized design of position-based bilateral control parameterized by complementary sensitivity function,” IEEE Trans. Ind. Electron. 65 (11), 8707–8717 (2018).Google Scholar
32. Zhu, W. H. and Salcudean, S. E., “Stability guaranteed teleoperation: An adaptive motion/force control approach,” IEEE Trans. Autom. Control 45 (11), 1951–1969 (2000).Google Scholar
33. Seki, K., Fujihara, S. and Iwasaki, M., “Improvement of force transmission performance considering nonlinear friction in bilateral control systems,” IFAC-PapersOnLine 50 (1), 12071–12076 (2017).Google Scholar
34. Shahruz, S. M., “Active vibration suppression in multi-degree-of-freedom systems by disturbance observers,” J. Viber. Control 15 (8), 1207–1228 (2009).Google Scholar
35. Ohnishi, K., Shibata, M. and Murakami, T., “Motion control for advanced mechatronics,” IEEE/ASME Trans. Mechatron. 1 (1), 56–67 (1996).Google Scholar
36. Saglam, C. O., Baran, E., Nergiz, A. O. and Sabanovic, A., “Model Following Control with Discrete Time SMC for Time-Delayed Bilateral Control Systems,” Proceedings of the IEEE International Conference on Mechatronics (2011) pp. 997–1002.Google Scholar
37. Kim, M. J. and Chung, W. K., “Disturbance-observer-based PD control of flexiblel joint robots for asymptotic convergence,” IEEE Trans. Robot. 31 (6), 1508–1516 (2015).Google Scholar
38. Murakami, T., Yu, F. and Ohnishi, K., “Torque sensorless control in multidegree-of-freedom manipulator,” IEEE Trans. Ind. Electron. 40 (2), 259–265 (1993).Google Scholar
39. Sariyildiz, E. and Ohnishi, K., “On the explicit robust force control via disturbance observer,” IEEE Trans. Ind. Electron. 62 (3), 1581–1589 (2015).Google Scholar
40. Sariyildiz, E. and Ohnishi, K., “A Comparison Study for Force Sensor and Reaction Force Observer Based Robust Force Control Systems,” Proceedings of the IEEE International Symposium on Industrial Electronics (2014) pp. 1156–1161.Google Scholar
41. Erickson, D., Weber, M. and Sharf, I., “Contact stiffness and damping estimation for robotic systems,” Int. J. Robot. Res. 22 (1), 41–57 (2003).Google Scholar
42. Araghi, M. H. and Salisbury, S. P., “Improved evaluation of dynamic mechanical properties of soft materials with applications to minimally invasive surgery,” IEEE/ASME Trans. Mechatron. 18 (3), 973–980 (2013).Google Scholar
43. Mitsantisuk, C., Ohishi, K. and Katsura, S., “Variable Mechanical Stiffness Control Based on Human Stiffness Estimation,” Proceedings of the IEEE International Conference on Mechatronics (2011) pp. 731–736.Google Scholar
44. Tsumugiwa, T., Yokogawa, R. and Hara, K., “Variable Impedance Control Based on Estimation of Human Arm Stiffness for Human-Robot Cooperative Calligraphic Task,” Proceedings of the IEEE International Conference on Robotics and Automation (2002) pp. 644–650.Google Scholar
45. Yang, Z., Poo, A. and Hong, G., “A New Method for Implementing Active Stiffness Control of Robot Manipulator by Using Sliding Mode,” Proceedings of the Asia-Pacific Workshop on Advances in Motion Control (1993) pp. 165–170.Google Scholar
46. Chiaverini, S., Siciliano, B. and Villani, L., “A survey of robot interaction control schemes with experimental comparison,” IEEE/ASME Trans. Mechatron. 4 (3), 273–285 (1999).Google Scholar
47. Han, Z. and Li, S., “Bp-Neural Network Alpha-Beta-Gamma Filter Optimized by GA,” Proceedings of the IEEE Conference on Industrial Electronics and Applications (2009) pp. 1952–1956.Google Scholar
48. Wu, C. M., Lin, P. P., Han, Z. Y. and Li, S. R., “Simulation-based optimal design of α–β–γ–δ filter,” Int. J. Automat. Comput. 7 (2), 247–253 (2010).Google Scholar
49. Blair, W., “Two-Stage Alpha-Beta-Gamma Estimator for Tracking Maneuvering Targets,” Proceedings of the American Control Conference (1992) pp. 842–846.Google Scholar
50. Crouse, D. F., “A general solution to optimal fixed-gain (-etc.) filters,” IEEE Signal Process. Lett. 22 (7), 901–904 (2015).Google Scholar

Baran et al. supplementary material

Baran et al. supplementary material 1

Download Baran et al. supplementary material(Video)
Video 19.9 MB