Hostname: page-component-7dd5485656-c8zdz Total loading time: 0 Render date: 2025-10-30T10:19:26.666Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptive control of a robot using neural networks

Published online by Cambridge University Press:  09 March 2009

D. T. Pham  and
S. J. Oh
D. T. Pham
Affiliation:
Intelligent Systems Laboratory, School of Electrical, Electronic and Systems Engineering, University of Wales College of Cardiff, P. O. Box 917, Newport Rd, Cardiff CF2 1XH (UK).
S. J. Oh
Affiliation:
Intelligent Systems Laboratory, School of Electrical, Electronic and Systems Engineering, University of Wales College of Cardiff, P. O. Box 917, Newport Rd, Cardiff CF2 1XH (UK).
Get access Rights & Permissions [Opens in a new window]

Summary

This paper describes an adaptive control system for an articulated robot with n joints carrying a variable load. The robot is a complex nonlinear time-varying MIMO plant with dynamic interaction between its inputs and outputs. However, the design of the control system is relatively straightforward and does not require any prior knowledge about the plant. This is because the control system is based on using neural networks which can capture the dynamic characteristics of the plant automatically. Three neural networks are employed in total, the first to learn the dynamics of the robot, the second to model its inverse dynamics and the third, a copy of the second neural network, to control the robot.

Keywords

MIMO systemNeural networkAdaptive controlRobots

Information

Type
Articles
Information
Robotica , Volume 12 , Issue 6 , November 1994 , pp. 553 - 561
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.)

Article purchase

Temporarily unavailable

References

1.Spong, M.W. and Vidyasagar, M.. Robot Dynamics and Control (John Wiley & Sons, NY, 1989).Google Scholar
2.Tomizuka, M., Horowitz, R., Anwar, G. and Jia, Y.L., “Implementation of adaptive techniques for motion controi of robotic manipulators” ASME J. Dynamical Systems, Measurement and Control 110, 62–69 (1988).CrossRefGoogle Scholar
3.Craig, J.J., Hsu, P. and Sastry, S.S., “Adaptive control of mechanical manipulators” Int. J. Robotics Research 6. No 2, 16–28 (1987).CrossRefGoogle Scholar
4.Siotine, J.J. and Li, W., “On the adaptive control of robot manipulators” Int. J. Robotics Research 6, No. 3, 49–59 (1987).Google Scholar
5.Khosla, P.K., “Real-time control and identification of direct-drive manipulators” PhD Thesis (Department of Electrical and Computer Engineering, Carnegie-Mellon University, 1986).Google Scholar
6.Miller, W.T.. Sutton, R.S. and Werbos, P.J., Neural Networks for Control (MIT Press, Cambridge, MA, 1990).CrossRefGoogle Scholar
7.Psaltis, D., Sideris, A. and Yamamura, A., “A multilayered neural network controller” IEEE Control Systems Magazine 17–21 (April, 1989).Google Scholar
8.Kawato, M., Uno, Y., Isobe, M. and Suzuki, R., “Hierarchical neural network model for voluntary movement with application to robotics” IEEE Control Systems Magazine 8–16 (April, 1988).CrossRefGoogle Scholar
9.Colombano, S.P., Compton, M. and Bualat, M., “Goal directed model inversion: adaptation to unexpected model changes” Proc. 4th International Conference on Neural Networks and Their Applications (NEURO-NIMES 91), Nimes, France, (1991) pp. 271–278.Google Scholar
10.Miller, W.T. III, Glanz, F.H. and Klaft, L.G. III, “Application of a general learning algorithm to the control of robotic manipulators” Int. J. of Robotics Research 6, No. 2, 84–98 (1987).CrossRefGoogle Scholar
11.Goldberg, K. and Pearlmutter, B.. “Using a neural network to learn the dynamics of the CMU Direct-Drive Arm II” Technical Report, CMU-CS-88-W (Carnegie Mellon University, 1988).Google Scholar
12.Ciliz, M.K., “Artificial neural network based control of nonlinear systems with application to robotic manipulators” PhD Thesis (Electrical Engineering, Syracuse Univ., USA, 1990).Google Scholar
13.Rung, S. Y. and Hwang, J.N., “Neural network architectures for robotic applications” IEEE Trans, on Robotics and Automation 5, No. 5, 641–657 (1991).Google Scholar
14.Ozaki, T., Suzuki, T., Furuhashi, T., Okuma, S. and Uchikawa, Y., “Trajectory control of robotic manipulators using neural networks” IEEE Trans, on Industrial Electronics 38, No. 3, 641–657 (1991).CrossRefGoogle Scholar
15.D'Souza, A.F.. Design of Control Systems (Prentice-Hall, Englewood Cliffs, NJ, 1988).Google Scholar
16.Kuo, B.. Automatic Control Systems (Prentice-Hall, Englewood Cliffs, NJ, 1982).Google Scholar
17.Pham, D.T. and Oh, S.J., “A recurrent backpropagation neural network for dynamic system identification” J. Systems Engineering 2, No. 4, 213–223 (1992).Google Scholar
18.Pham, D.T. and Oh, S.J.. “Inverse system identification of dynamic systems using neural networks” Research Report (School of Electrical, Electronic and Systems Eng., Univ. Wales, UK).Google Scholar
19.Pham, D.T. and Oh, S.J., “Adaptive control of dynamic systems using neural networks”, Proc. of IEEE-SMC Conference, Le Touquet, France (Oct., 1993) 4, pp. 99–102.Google Scholar
20.Makino, H.. Furuya, N., Soma, K. and Chin, E., “Research and development of the Scara robot” Proc. 4th Int. Conf. on Production Engineering, Tokyo (1980) pp. 885–890.Google Scholar
21.Gerald, C.F. and Wheatley, P.D.. Applied Numerical Analysis (Addison-Wesley, Reading, England, 1989).Google Scholar
22.Craig, J.J.. Introduction to Robotics: Mechanics and Control (Addison-Wesley, Reading, MA, 1989)Google Scholar