Impedance Control and Its Adaptive for Hexapod Robot

  • Kenzo Nonami
  • Ranjit Kumar Barai
  • Addie Irawan
  • Mohd Razali Daud
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 66)


Impedance control should be generally applied for most robot when contacts some environment to achieve “softness of contact” between the end-effector of robot and the environment. This chapter proposes several algorithms such as impedance control implementation for hexapod robot COMET-IV. Also, in the case of heavyweight and large-scale-structured robot, inclinometers from attitude angles should be designed to control the long-term attitudes of the body, not to prevent shaking caused by changes in support of the legs. Moreover, this shaking is considered as a natural scenario since the robot is using hydraulic system and automotive engine. Therefore, only attitude errors that are over the acceptable limit will be included in the adaptive calculation.


Impedance Control Linear Quadratic Regulator Attitude Feedback Robot Body Virtual Force 


  1. 1.
    Palis F, Rusin V (2004) Adaptive impedance control of legged robot. Paper presented at the proceeding of 11th international conference on power electronics and motion control, RigaGoogle Scholar
  2. 2.
    Erickson D, Weber M, Sharf I (2003) Contact stiffness and damping estimation for robotic systems. Int J Robotics Res 22(1)Google Scholar
  3. 3.
    Lehtinen H (1994) Force based motion control of a walking machine. VTT Publication, EspooGoogle Scholar
  4. 4.
    Irawan A, Nonami K (2011) Optimal impedance control based on body inertia for a hydraulically driven hexapod robot walking on uneven and extremely soft terrain. J Field Robotics 28(5):690–713CrossRefMATHGoogle Scholar
  5. 5.
    Futagami K, Harada Y, Oku M, Ohroku H, Lin X, Okawa K, Sakai S, Nonami K (2008) Real-time navigation and control of hydraulically actuated hexapod robot COMET-IV. In: Proceeding of the 9th international conference on motion and vibration control 2008 (MOVIC 2008), Munich, 2008Google Scholar
  6. 6.
    Ohroku H, Futagami K, Harada Y, Oku M, Nonami K (2008) Tele-operation and navigation of hexapod robot COMET-IV with real-time gait and trajectory planning. In: 9th international conference on motion and vibration control 2008 (MOVIC 2008), Munich, 2008Google Scholar
  7. 7.
    Oku M, Koseki H, Ohroku H, Harada Y, Futagami K, Tran DC, Li L, Lin X, Sakai S, Nonami K (2008) Rough terrain locomotion control of hydraulically actuated hexapod robot COMET-IV (in Japanese). In: JSME conference on robotics and mechatronics 2008 (ROBOMEC 2008), Nagano, 2008Google Scholar
  8. 8.
    Hespanha JP (2005) Lecture notes on LQR/LQG controller designGoogle Scholar
  9. 9.
    Terashima K, Miyoshi T, Mouri K, Kitagawa H, Minyong P (2009) Hybrid impedance control of massage considering dynamic interaction of human and robot collaboration systems. J Robotics Mechatronics 21(1)Google Scholar
  10. 10.
    Jung S, Hsia TC, Bonitz RG (2001) Force tracking impedance control for robot manipulators with an unknown environment: theory, simulation, and experiment. Int J Robotics Res 20(9):765–774CrossRefGoogle Scholar
  11. 11.
    Kikuuwe R, Yoshikawa T (2002) Robot perception of environment impedance. In: Proceeding of IEEE international conference of robotics and automation 2002 (ICRA 2002), Kyoto, pp 1661–1666Google Scholar
  12. 12.
    Barai R, Nonami K (2007) Optimal two-degree-of-freedom fuzzy control for locomotion control of a hydraulically actuated hexapod robot. Info Sci Appl 117(8):1892–1915CrossRefGoogle Scholar
  13. 13.
    Irawan A, Nonami K, Ohroku H, Akutsu Y, Imamura S (2011) Adaptive impedance control with compliant body balance for hydraulically driven hexapod robot. J Syst Des Dyn 5(5):893–908, Special issue of motion and vibration control 2010Google Scholar
  14. 14.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybernatics 15(1):116–132CrossRefMATHGoogle Scholar
  15. 15.
    Cox E, O'Hagan M (1999) The fuzzy systems handbook: a practitioner's guide to building, using, and maintaining fuzzy systems, 2nd edn. Academic, CambridgeGoogle Scholar
  16. 16.
    Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Set Syst 28:15–33CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Castillo O, Melin P (2008) Type-2 fuzzy logic theory and applications. Springer, BerlinMATHGoogle Scholar
  18. 18.
    Vasickaninova A, Bakasova M (2010) Locally optimal fuzzy control of a heat exchanger. WSEAS Trans Syst 9(9):999–1008Google Scholar
  19. 19.
    Daud MR, Nonami K (2011) LRF assisted force-based walking for hexapod robot COMET-IV. Int J Automation Robotics Autonomous Syst (ARAS) 11(1):11–22Google Scholar
  20. 20.
    Daud MR, Nonami K, Irawan A (2011) LRF assisted autonomous walking in rough terrain for hexapod robot COMET-IV. In: Proceeding of the international conference on intelligent unmanned systems 2011 (ICIUS 2011), Chiba, 2011Google Scholar

Copyright information

© Springer Japan 2014

Authors and Affiliations

  • Kenzo Nonami
    • 1
  • Ranjit Kumar Barai
    • 2
  • Addie Irawan
    • 3
  • Mohd Razali Daud
    • 3
  1. 1.Department of Mechanical Engineering Division of Artificial Systems Science Graduate School of EngineeringChiba UniversityChibaJapan
  2. 2.Department of Electrical EngineeringJadavpur UniversityKolkataIndia
  3. 3.Faculty of Electrical and Electronics Engineering Robotics and Unmanned Systems (RUS) groupUniversiti Malaysia PahangPahangMalaysia

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