Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-15T09:59:37.006Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning control for robot motion under geometric end-point constraint

Published online by Cambridge University Press:  09 March 2009

Suguru Arimoto  and
Tomohide Naniwa
Suguru Arimoto
Affiliation:
Faculty of Engineering, University of Tokyo, Bunkyo-ku, Tokyo, 113(Japan)
Tomohide Naniwa
Affiliation:
Faculty of Engineering, University of Tokyo, Bunkyo-ku, Tokyo, 113(Japan)
Get access Rights & Permissions [Opens in a new window]

Summary

Learning control is a new approach to the probelm of skill refinement for robotic manipulators. It is considered to be a mathematical model of motor program learning for skilled motions in the central nervous system.

This paper proposes a class of learning control algorithms for improving operations of the robot arm under a geometrical end-point constraint at the next trial on the basis of the previous operation data. The command input torque is updated by a linear modification of present joint velocity errors deviated from the desired velocity trajectory in addition to the previous input. It is shown that motion trajectories approach an e-neighborhood of the desired one in the sense of squared integral norm provided the local feedback loop consists of both position and velocity feedbacks plus a feedback term of the error force vector between the reactive force and desired force on the end-point constrained surface. It is explored that various passivity properties of residual error dynamics of the manipulator play a crucial role in the proof of uniform boundedness and convergence of position and velocity trajectories.

Keywords

Learning controlRobot motionSkill refinementEnd-point constraint
Type
Article
Information
Robotica , Volume 12 , Issue 2 , March 1994 , pp. 101 - 108
Copyright
Copyright © Cambridge University Press 1994

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.Arimoto, S., Kawamura, S. and Miyazaki, F., “Bettering operation of robots by learning” J. Robotic Systems 1, 123–140 (1984).CrossRefGoogle Scholar
2.Arimoto, S., Kawamura, S. and Miyazaki, F., “Bettering operation of dynamic systems by learning; A new control theory for servomechanism and mechatronics systems” Proc. 23rd IEEE Conf. Decision and Control, Las Vegas, NV (1984) pp. 1064–1069.Google Scholar
3.Arimoto, S., Kawamura, S. and Miyazaki, F. “Can mechanical robots learn by themselves?” Robotics Research The second International Symposium (Hanafusa, H. & Inoue, H., Eds.) (MIT Press, Cambridge, Massachusetts (1985)) pp. 1566–1573.Google Scholar
4.Craig, J.J., “Adaptive control of manipulators through repeated trials” Procs. of 1984 American Control Conference, San Diego, California (1984) pp. 206–213.Google Scholar
5.Bondi, P., Casalno, G. and Gambardella, L., “On the iterative learning control theory for robotic manipulators” IEEE J. of Robotics and Automation 4, 14–22 (1988).CrossRefGoogle Scholar
6.Arimoto, S., “Mathematical theory of learning with applications to robot control” Procs. of 4th Yale Workshop on Applications of Adaptive Systens Theory, Yale University, New Haven, Connecticut, (1985) pp. 379–388.Google Scholar
7.Kawamura, S., Miyazaki, F. and Arimoto, S., “Hybrid position/force control of manipulators based on learning method” Proc. '85 Inter. Conf. on Advanced Robotics, Tokyo, (1985) pp. 235–242.Google Scholar
8.Kawamura, S., Miyazaki, F. and Arimoto, S., “Realization of robot motion based on a learning method” IEEE Trans. on Systems, Man, and Cybernetics SMC-18, 126–134 (1988).CrossRefGoogle Scholar
9.Arimoto, S., “Learning control theory for robotic motion” Int. J. Adaptive Control and Signal Processing 4, 543–564 (1990).CrossRefGoogle Scholar
10.Arimoto, S. and Miyazaki, F., “Stability and robustness of PID feedback control for robot manipulators of sensory capability” Robotics Research: The First International Symposium (Brady, M. & Paul, R.P., Eds.) (MIT Press, Cambridge, Massachusetts 1984) pp. 783–799.Google Scholar
11.Arimoto, S. and Miyazaki, F., “Asymptotic stability of feedback control laws for robot manipulators” Proc. IFAC Symp. on Robot Control '85, Barcelona, Spain (1985) pp. 221–226.Google Scholar
12.Koditschek, D.E., “Natural motion for robot arms” Proc. of 23rd IEEE Conf. Decision and Control, Las Vegas, NV, (1987) pp. 731–735.Google Scholar
13.Arimoto, S., Kawamura, S. and Miyazaki, F., “Convergence, stability, and robustness of learning control schemes for robot manipulator” In: (Jamshida, M.J., Luh, L.Y.S. and Shahinpoor, M., eds.) Recent Trends in Robotics: Modeling, Control, and Education (Elsevier Sciences Publishing Co., New York, 1986) pp. 307–316.Google Scholar
14.Heizinger, G., Fenwick, D., Paden, B. and Miyazaki, F., “Robust learning control” Proc. 28th IEEE Conf. Decision and Control, Tampa, Florida, Dec. 13–15, 1989 (1989) pp. 436–440.Google Scholar
15.Arimoto, S., Naniwa, T. and Suzuki, H., “Robustness of P-type Learning control with a forgetting factor for robotic motions” Procs. of 29th IEEE Conference on Decision and Control, Honolulu, Hawaii, Dec. 5-7, 1990 (1990) pp. 2640–2645.Google Scholar
16.Arimoto, S., “Learning for skill refinement in robotic systems” IEICE (Institute of Electronics, Information and Communication Engineers) Transactions E74, No. 2, 235–243 (1991).Google Scholar
17.Arimoto, S., “Passivity of robot dynamics implies capability of motor program learning” Advanced Robot Control' Procs. of the Workshop on Nonlinear and Adaptive Control: Applications to Robotics, Grenoble, France Nov. 21–23, 1990 (springer-Verlag, Berlin, 1991) pp. 49–68.Google Scholar
18.Nanjo, Y. and Arimoto, S., “Experimental studies on robustness of a learning method with a forgetting factor for robotic motion control” Proc. of '91 ICAR, Pisa, Italy, June 19–22, 1991 (1991) pp. 699–704.Google Scholar
19.Arimoto, S. and Naniwa, T., “Learning control for geometrically constrained robot manipulators” Proc. of 1993 IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Yokahama, Japan,July 26–30, 1993 (1993) pp. 746–753.Google Scholar