Skip to main content
SpringerLink
Log in

Model reference adaptive impedance control for physical human-robot interaction

  • Published:
Control Theory and Technology Aims and scope Submit manuscript

Abstract

This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. J. Wainer, D. J. Feil-Seifer, D. A. Shell, et al. The role of physical embodiment in human-robot interaction. The 15th IEEE International Symposium on Robot and Human Interactive Communication, Hatfield: IEEE, 2006: 117–122.

    Google Scholar 

  2. F. L. Lewis, D. Dawson, M. Abdallah, et al. Robot Manipulator Control: Theory and Practice. Boca Raton: CRC Press, 2003.

    Book  Google Scholar 

  3. J. J. E. Slotine, W. Li. Applied Nonlinear Control, Englewood Cliffs: Prentice Hall, 1991.

    MATH  Google Scholar 

  4. M. Jamshidi, B. J. Oh, H. Seraji. Two adaptive control structures of robot manipulators. Journal of Intelligent and Robotic Systems, 1992, 6(2/3): 203–218.

    Google Scholar 

  5. N. Hogan. Impedance control: an approach to manipulation. Proceedings of the American Control Conference, San Diego: IEEE, 1984: 304–313.

    Google Scholar 

  6. R. Anderson, M. W. Spong. Hybrid impedance control of robotic manipulators. Journal Robot Automation, 1988, 4(5): 549–556.

    Article  Google Scholar 

  7. H. Kawasaki, R. Taniuchi. Adaptive control for robotic manipulators executing multilateral constrained task. Asian Journal of Control, 2003, 5(1): 1–11.

    Article  Google Scholar 

  8. H. Wu, W. Xu, C. Cai. Adaptive impedance control in robotic cell injection system. The 17th International Conference on Methods and Models in Automation and Robotics, Miedzyzdrojie: IEEE, 2012: 268–275.

    Google Scholar 

  9. S. S. Ge, C. C. Hang, T. H. Lee, et al. Stable Adaptive Neural Network Control. Boston: Kluwer Academic. 2001.

    Google Scholar 

  10. M. K. Vukobratovic, A. G. Rodic, Y. Ekalo. Impedance control as a particular case of the unified approach to the control of robots interacting with a dynamic known environment. Journal of Intelligent & Robotic Systems, 1992, 18(2): 191–204.

    Article  Google Scholar 

  11. E. Gribovskaya, A. Kheddar, A. Billard. Motion learning and adaptive impedance for robot control during physical interaction with humans. IEEE International Conference on Robotics and Automation, Shanghai: IEEE, 2011: 4326–4332.

    Google Scholar 

  12. L. Huang, S. S. Ge, T. H. Lee. Neural network adaptive impedance control of constrained robots. IEEE International Symposium on Intelligent Control, Vancouver: IEEE, 2002: 615–619.

    Chapter  Google Scholar 

  13. C. Wang, Y. Li, S. Ge, et al. Continuous critic learning for robot control in physical human-robot interaction. Proceedings of the 13th International Conference on Control, Automation and Systems, Gwangju: IEEE, 2013: 833–838.

    Google Scholar 

  14. Y. Li, S. S. Ge, C. Yang. Impedance control for multi-point human-robot interaction. Proceedings of the 8th Asian Control Conference, Kaohsiung: IEEE, 2011: 1187–1192.

    Google Scholar 

  15. T. Tsuji, Y. Tanaka. Tracking control properties of human-robotic systems based on impedance control. IEEE Transactions on Systems, Man and Cybernetics–Part A: Systems and Humans, 2005, 35(4): 523–535.

    Article  Google Scholar 

  16. K. Doya, H. Kimura, M. Kawato. Neural mechanisms of learning and control. IEEE Control Systems Magazine, 2001, 21(4): 42–54.

    Article  Google Scholar 

  17. D. Wolpert, M. Miall, R. Chris, et al. Internal models in the cerebellum. Trends in Cognitive Sciences, 1998, 2(9): 338–347.

    Article  Google Scholar 

  18. D. Kleinman, L. Baron, S. Baron, et al. An optimal control model of human response–Part I: Theory and validation. Automatica, 1970, 6(3): 357–369.

    Article  Google Scholar 

  19. R. C. Miall, D. J. Weir, D. M. Wolpert, et al. Is the cerebellum a smith predictor. Journal of Motor Behavior, 1993, 25(3): 203–216.

    Article  Google Scholar 

  20. S. Suzuki, K. Kurihara, K. Furuta, et al. Variable dynamic assist control on haptic system for human adaptive mechatronics. Proceedings of the 44th IEEE Conference on Decision Control/ European Control Conference, New York: IEEE, 2005: 4596–4600.

    Chapter  Google Scholar 

  21. S. Suzuki, K. Furuta. Adaptive impedance control to enhance human skill on a haptic interface system. Journal of Control Science and Engineering, 2012: DOI 10.1155/2012/365067.

    Google Scholar 

  22. F. C. Chen, H. K. Khalil. Adaptive control of nonlinear systems using neural networks. International Journal Control, 1992, 55(6): 1299–1317.

    Article  MathSciNet  MATH  Google Scholar 

  23. F. L. Lewis, S. Jagannathan, A. Yesildirek. Neural Network Control of Robot Manipulators and Nonlinear Systems. London: Taylor and Francis, 1992.

    Google Scholar 

  24. S. S. Ge, T. H. Lee, C. J. Harris. Adaptive Neural Network Control of Robotic Manipulators. Singapore: World Scientific, 1998.

    Book  Google Scholar 

  25. G. A. Christodoulou, M. A. Rovithakis. Adaptive control of unknown plants using dynamical neural networks. IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans, 1994, 24(3): 400–412.

    Article  MathSciNet  Google Scholar 

  26. A. Ye¸sildirek, F. L. Lewis. Feedback linearization using neural networks. Automatica, 1995, 31(11): 1659–1664.

    Article  MathSciNet  Google Scholar 

  27. M. M. Polycarpou. Stable adaptive neural control scheme for nonlinear systems. IEEE Transactions on Automatic Control, 1996, 14(3): 447–451.

    Article  MathSciNet  Google Scholar 

  28. A. S. Poznyak, W. Yu, E. N. Sanchez, et al. Nonlinear adaptive trajectory tracking using dynamic neural networks. IEEE Transactions on Neural Networks, 1996, 10(6): 1402–1411.

    Article  Google Scholar 

  29. G. A. Rovithakis. Performance of a neural adaptive tracking controller for multi-input nonlinear dynamical systems. IEEE Transactions on Systems, Man, Cybernetics–Part A: Systems and Humans, 2000, 30(6): 720–730.

    Article  Google Scholar 

  30. R. Hunt, K. J. Zbikowski. Neural Adaptive Control Technology. Singapore: World Scientific, 1996.

    MATH  Google Scholar 

  31. P. V. Osburn, H. P. Whitaker, A. Kezer. New Developments in the Design of Model Reference Adaptive Control Systems. Easton: Institute of Aeronautical Sciences, 1961.

    Google Scholar 

  32. P. Parks. Lyapunov redesign of model reference adaptive control systems. IEEE Transactions on Automatic Control, 1966, 11(3): 362–367.

    Article  Google Scholar 

  33. K. J. Åström, B. Wittenmark. A survey of adaptive control applications. Proceedings of the 34th IEEE Conference on Decision and Control, New Orleans: IEEE, 1995: 649–654.

    Google Scholar 

  34. J. Aseltine, A. Mancini, C. Sarture. A survey of adaptive control systems. IEEE Transactions on Automatic Control, 1958, 6(1): 102–108.

    Article  Google Scholar 

  35. H. Unbehauen. Adaptive dual control systems: A survey. Adaptive Systems for Signal Processing, Communications, and Control Symposium, Lake Louise: IEEE, 2000: 171–180.

    Google Scholar 

  36. N. M. Filatov, H. Unbehauen. Survey of adaptive dual control methods. IEEE Proceedings Control Theory and Applications, 2000, 147(1): 118–128.

    Article  Google Scholar 

  37. K. J. Åström, B. Wittenmark. Adaptive Control. India: Pearson Education, 2001.

    Google Scholar 

  38. I. D. Landau. A survey of model reference adaptive techniques theory and applications. Automatica, 1974, 10(4): 353–379.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Texas at Arlington Research Institute (UTARI), Fort Worth, TX, 76118, USA

    Bakur Alqaudi, Hamidreza Modares, Isura Ranatunga, Shaikh M. Tousif, Frank L. Lewis & Dan O. Popa

  2. Department of Electrical Engineering, College of Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA

    Bakur Alqaudi, Hamidreza Modares, Isura Ranatunga, Shaikh M. Tousif, Frank L. Lewis & Dan O. Popa

  3. Electronics and Instrumentation Engineering Technology Department, Yanbu Industrial College, P.O. Box 30436, 41912, Yanbu al-Sinaiyah, K.S.A.

    Bakur Alqaudi

  4. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang Liaoning, 110819, China

    Frank L. Lewis

Authors
  1. Bakur Alqaudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamidreza Modares
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Isura Ranatunga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaikh M. Tousif
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Frank L. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dan O. Popa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bakur Alqaudi.

Additional information

The work was supported by the National Science Foundation (No. IIS-1208623), the Office of Naval Research grant (No. N00014-13-1-0562), the AFOSR (Air Force Office of Scientific Research) EOARD (European Office of Aerospace Research and Development) grant (No. 13-3055), the U.S. Army Research Office grant (No. W911NF-11-D-0001).

Bakur ALQAUDI received the B.Sc. degree in Electronics Commination and Electrical Automation from the Yanbu Industrial College, Yanbu, Saudi Arabia, and M.Sc. degree in Electrical Engineering focusing in Biorobotics, Control and Cybernetics from Rochester Institute of Technology, Rochester, NY, U.S.A., in 2008 and 2012, respectively. He is currently pursuing the Ph.D. degree with the University of Texas at Arlington, Arlington, TX, U.S.A. He joined Yanbu Industrial College as an Instructor, from 2008 to 2009, and received the King’s scholarship for Gas and Petroleum track in 2009. His current research interests include physical human-robot interaction, adaptive control, reinforcement learning, robotics, and cognitive-psychological inspired learning and control.

Hamidreza MODARES received the B.Sc. degree from the University of Tehran, Tehran, Iran, and the M.S. degree from the Shahrood University of Technology, Shahrud, Iran, in 2004 and 2006, respectively. He is currently pursuing the Ph.D. degree with the University of Texas at Arlington, Arlington, TX, U.S.A. He joined the Shahrood University of Technology as a University Lecturer, from 2006 to 2009. Since 2012, he has been a Research Assistant with the University of Texas at Arlington Research Institute, Fort Worth, TX, U.S.A. His current research interests include optimal control, reinforcement learning, distributed control, robotics, and pattern recognition.

Isura RANATUNGA (S’09) received the B.Sc. degree from the University of Texas at Arlington, Arlington, TX, U.S.A., in 2010, where he is currently pursuing the Ph.D. degree with a focus on robotics and automation, both in Electrical Engineering. He is a Graduate Research Assistant with the University of Texas at Arlington Research Institute, Fort Worth, TX, U.S.A., and the Next Generation Systems Research Group. His current research interests include force control, physical human-robot interaction, bipedal walking, adaptive robot control, and autonomous navigation.

Shaikh M. TOUSIF received the B.Sc. degree in Electrical and Electronic Engineering from American International University- Bangladesh, Bangladesh in 2009, and the M.Sc. degree in Electrical Engineering from University of Texas at Arlington in 2014. From 2009 to 2012 he was a lecturer in the Department of Electrical Engineering in American International University- Bangladesh and responsible for teaching many engineering courses. He is currently pursuing his Ph.D. at University of Texas at Arlington, Texas, U.S.A.

Frank L. Lewis (S’70-M’81-SM’86-F’94) received the B.Sc. degree in Physics and Electrical Engineering and the M.S.E.E. degree, both from Rice University, Houston, TX, U.S.A., the M.Sc. degree in Aeronautical Engineering from the University of West Florida, Pensacola, FL, U.S.A., and the Ph.D. degree from the Georgia Institute of Technology, Atlanta, GA, U.S.A. He is a University of Texas at Arlington Distinguished Scholar Professor, a Teaching Professor, and the Moncrief-O’Donnell Chair with the University of Texas at Arlington Research Institute, Fort Worth, TX, U.S.A. He is the Qian Ren Thousand Talents Consulting Professor with Northeastern University, Shenyang, China. He is a Distinguished Visiting Professor with the Nanjing University of Science and Technology, Nanjing, China, and the Project 111 Professor with Northeastern University. His current research interests include feedback control, intelligent systems, cooperative control systems, and nonlinear systems. He has authored numerous journal special issues, journal papers, 20 books, including Optimal Control, Aircraft Control, Optimal Estimation, and Robot Manipulator Control, which are used as university textbooks worldwide and he holds six U.S. patents. Dr. Lewis was a recipient of the Fulbright Research Award, the National Science Foundation Research Initiation Grant, the American Society for Engineering Education Terman Award, the International Neural Network Society Gabor Award, the U.K. Institute of Measurement and Control Honeywell Field Engineering Medal, the IEEE Computational Intelligence Society Neural Networks Pioneer Award, the Outstanding Service Award from Dallas IEEE Section, and selected as an Engineer of the Year by the Fort Worth IEEE Section. He was listed in Fort Worth Business Press Top 200 Leaders in Manufacturing and Texas Regents Outstanding Teaching Award in 2013. He is a PE of Texas and a U.K. Chartered Engineer. He is a member of the National Academy of Inventors and a fellow of International Federation of Automatic Control and the U.K. Institute of Measurement and Control. He is a Founding Member of the Board of Governors of the Mediterranean Control Association. Board of Governors of the Mediterranean Control Association.

Dan O. POPA (M’93) received the B.A. degree in Engineering, Mathematics, and Computer Science and the M.S. degree in Engineering, both from Dartmouth College, Hanover, NH, U.S.A., and the Ph.D. degree in Electrical, Computer and Systems Engineering from Rensselaer Polytechnic Institute (RPI), Troy, NY, U.S.A., in 1998, focusing on control and motion planning for nonholonomic systems and robots. He is an Associate Professor with the Department of Electrical Engineering, University of Texas at Arlington, and the Head of the Next Generation Systems Research Group. He joined the Center for Automation Technologies at RPI, where he was a Research Scientist until 2004, for over 20 industry-sponsored projects. He was an Affiliated Faculty Member of the University of Texas at Arlington Research Institute, Fort Worth, TX, U.S.A., and a Founding Member of the Texas Microfactory Initiative, in 2004. His current research interests include the simulation, control, packaging of microsystems, the design of precision robotic assembly systems, and control and adaptation aspects of human-robot interaction. He has authored over 100 refereed publications. Dr. Popa was a recipient of several prestigious awards, including the University of Texas Regents Outstanding Teaching Award. He serves as an Associate Editor of the IEEE Transaction On Automation Science and Engineering and the Journal of Micro and Bio Robotics (Springer). He is an active member of the IEEE Robotics and Automation Society Conference Activities Board, the IEEE Committee on Micro-Nano Robotics, and the ASME Committee on Micro-Nano Systems and a member of ASME.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alqaudi, B., Modares, H., Ranatunga, I. et al. Model reference adaptive impedance control for physical human-robot interaction. Control Theory Technol. 14, 68–82 (2016). https://doi.org/10.1007/s11768-016-5138-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11768-016-5138-2

Keywords

Search

Navigation