Online learning of virtual impedance parameters in non-contact impedance control using neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 34, Issue 5, 2004, Pages 2112-2118

  Tsuji, Toshio ^a   Terauchi, Mutsuhiro ^b   Tanaka, Yoshiyuki ^a  

^a HIROSHIMA UNIVERSITY   (Japan)

^b HIROSHIMA INTERNATIONAL UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; COMPUTER SIMULATION; END EFFECTORS; EQUATIONS OF MOTION; MANIPULATORS; MATRIX ALGEBRA; MOTION CONTROL; ONLINE SYSTEMS; ROBOT LEARNING;

IMPACT CONTROL; IMPEDANCE CONTROL; NONCONTACT IMPEDANCE; ROBOT MANIPULATOR;

NEURAL NETWORKS;

CYCLOPEPTIDE; VISCOSIN;

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPARATIVE STUDY; ELASTICITY; EVALUATION; FEEDBACK SYSTEM; IMPEDANCE; LETTER; MECHANICAL STRESS; METHODOLOGY; MOTION; ONLINE SYSTEM; ROBOTICS; STIMULATION; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELASTICITY; ELECTRIC IMPEDANCE; FEEDBACK; MOTION; NEURAL NETWORKS (COMPUTER); ONLINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED; PEPTIDES, CYCLIC; PHYSICAL STIMULATION; ROBOTICS; STRESS, MECHANICAL;

EID: 4844227900     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2004.829133     Document Type: Article

References (25)

1. N. Hogan, "Impedance control: An approach to manipulation, parts I, II, III," Trans. ASME, J. Dyn. Syst., Meas. Contr, vol. 107, no. 1, pp. 1-24, 1985.
2. N. Hogan, "Stable execution of contact tasks using impedance control," in Proc. IEEE Int. Conf. Robotics Automation, 1987, pp. 1047-1054.
3. A. Castano and S. Hutchinson, "Visual compliance: Task-directed visual servo control," IEEE Trans. Robot. Automar., vol. 10, pp. 334-342, June 1994.
4. T. Tsuji and M. Kaneko, "Non-contact impedance control for redundant manipulator," IEEE Trans. Syst., Man, Cybern. A, vol. 29, pp. 184-193, Mar. 1999.
5. Y. Nakabo and M. Ishikawa, "Visual impedance using 1 ms visual feedback system," in Proc. IEEE Int. Conf. Robotics Automation, 1998, pp. 2333-2338.
6. M. Tokita, T. Mitsuoka, T. Fukuda, T. Shibata, and F. Arai, "Position and force hybrid control of a robotic manipulator by application of a neural network," J. Math. Model. Sci. Comput., vol. 1, no. 3-4, pp. 235-246, 1993.
7. H.-L. Pei, Q.-J. Zhou, and T. P. Leung, "A neural network robot force controller," in Proc. IEEE/RSJ Int. Conf. Intelligent Robots Systems, 1992, pp. 1974-1979.
8. T. H. Connolly and F. Pfeiffer, "Neural network hybrid position/force control," in Proc. IEEE/RSJ Int. Conf. Intelligent Robots Systems, 1993, pp. 240-244.
9. J. M. Tao and J. Y. S. Luh, "Application of neural network with real-time training to robust position/force control of multiple robots," in Proc. IEEE Int. Conf. Robotics Automation, 1993, pp. 142-148.
10. K. Kiguchi and T. Fukuda, "Position/force control of robot manipulators for geometrically unknown objects using fuzzy neural networks," IEEE Trans. Ind. Electron., vol. 47, pp. 641-649, June 2000.
11. G. Gomi and M. Kawato, "Neural network control for a closed loop system using feedback-error-learning," Neural Networks, vol. 6, no. 7, pp. 933-946, 1993.
12. D. Katiæ and M. Vukobratoviæ, "Learning impedance control of manipulation robots by feedforward connectionist structures," in Proc. IEEE Int. Conf. Robotics Automation, 1994, pp. 45-50.
13. C. C. Cheah and D. Wang, "Learning impedance control for robotic manipulators," IEEE Trans. Robot. Automat., vol. 14, no. 3, pp. 452-465, June 1998.
14. S. Jung and T. C. Hsia, "Neural network impedance force control of robot manipulator," IEEE Trans. Ind. Electron., vol. 45, no. 3, pp. 452-465, June 1998.
15. S.-T. Lin and H.-C. Tsai, "Impedance control-with online neural network compensator for dual-arm robots," J. Intell. Robot. Syst., vol. 18, pp. 87-104, 1997.
16. S. Jung and T. C. Hsia, "Robust neural force control scheme under uncertainties in robot dynamics and unknown environment," IEEE Trans. Ind. Electron., vol. 47, no. 2, pp. 403-412, Apr. 2000.
17. S. Jung, S. B. Yim, and T. C. Hsia, "Experimental studies of neural network impedance force control for robot manipulators," in Proc. IEEE Int. Conf. Robotics Automation, 2001, pp. 3453-3458.
18. H. Asada, "Teaching and learning of compliance using NeuralNets: Representation and generation of nonlinear compliance," in Proc. IEEE Int. Conf. Robotics Automation, 1990, pp. 1237-1244.
19. M. Cohen and T. Flash, "Learning impedance parameters for robot control using an associative search networks," IEEE Trans. Robot. Automat., vol. 7, pp. 382-390, June 1991.
20. B.-H. Yang and H. Asada, "Progressive learning and its application to robot impedance learning," IEEE Trans. Neural Networks, vol. 7, pp. 941-952, May 1996.
21. T. Tsuji, M. Nishida, and K. Ito, "Iterative learning of impedance parameters for manipulator control using neural networks," Trans. Soc. Instrum. Contr. Eng., vol. 28, no. 12, pp. 1461-1468, 1992.
22. T. Tsuji, K. Ito, and P. G. Morasso, "Neural network learning of robot arm impedance in operational space," IEEE Trans. Syst., Man, Cybern. B, vol. 26, pp. 290-298, Apr. 1996.
23. T. Tsuji, K. Harada, H. Akamatsu, and M. Kaneko, "On-line learning of robot arm impedance using neural networks," J. Robot. Soc. Jpn., vol. 17, no. 2, pp. 234-241, 1999.
24. K. Kitagaki and M. Uchiyama, "Optimal approach velocity of an end-effector to the environment," Adv. Robot., vol. 8, no. 2, pp. 123-137, 1994.
25. Y. Shoji, M. Inaba, and T. Fukuda, "Stable force control of 1 degree-of-freedom manipulator in contact tasks," in Proc. IEEE/RSJ Int. Conf. Intelligent Robots Systems, 1993, pp. 1511-1515.