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A model algorithmic learning method for continuous-path control of a robot manipulator

Published online by Cambridge University Press:  09 March 2009

Sang–Rok Oh ,
Zeungnam Bien  and
Il Hong Suh
Sang–Rok Oh
Affiliation:
Power Controls Lab., Kaist P.O. Box 131, Chongyangni, Seoul 136–791 (Korea).
Zeungnam Bien
Affiliation:
Dept. of Electrical Engineering, Kaist, Seoul 130–650 (Korea).
Il Hong Suh
Affiliation:
Dept. of Electronic Engineering, Hanyang Univ., Seoul (Korea).
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Summary

A new type of an iterative learning control method is proposed for dynamic systems with uncertain parameters. The method, which employs the model algorithmic control concept in the iteration sequence, is shown to be convergent for linear time-varying systems. Then the method is shown to be applicable for continuous-path control of a robot manipulator.

Keywords

Algorithmic learningContinuous pathControlRobot
Type
Article
Information
Robotica , Volume 8 , Issue 1 , January 1990 , pp. 31 - 36
Copyright
Copyright © Cambridge University Press 1990

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References

1.Lee, C.S.G., Gonzalez, R.C. and Fu, K.S., Tutorial on Robotics (IEEE Computer Society Press, Los Angeles, 1983).Google Scholar
2.Makowski, K. and Neustadt, L.W., “Optimal Control Problems with Mixed Control-Phase Variable Equality and Inequality Constraints” SIAM J. Control 12, 184–228 (1974).Google Scholar
3.Raibert, M.H., “Motor Control and Learning by the State Space Model” Ph.D. Dissertation (Dept. of Psychology, MIT 1977).Google Scholar
4.Arimoto, S., Kawamura, S. and Miyazaki, F., “Bettering Operation of Robots by Learning” J. Robotics Systems 1, No. 2, 123–140 (1984).Google Scholar
5.Arimoto, S., Kawamura, S. and Miyazaki, F., “Can Mechanical Robots Learn by Themselves?” Proc. 2nd Int. Symp. Robotics Res. 127–134, Kyoto, Japan (08, 1984).Google Scholar
6.Rouhani, R. and Mehra, R.K., “Model Algorithmic Control (MAC): Basic theoretical Properties” Automatica 18, No. 4, 401–414 (1982).Google Scholar
7.Richalet, J., Rault, A., Testud, J.L. and Papon, J., “Model Predictive Heuristic Control: Application to Industrial Processes” Automatica 14, 413–428 (1978).Google Scholar
8.Luenberger, D.G., Optimization by Vector Space Methods (John Wiley & Sons, New York, 1969).Google Scholar
9.Fleming, W.H., Functions of Several Variable (Addison-Wesley, Massachusetts, 1965).Google Scholar
10.Mehra, R.K., Rouhani, R. and Praly, L., “New Theoretical Developments in Multivariable Predictive Algorithmic Control” Proc. JACC FA9-B (1980).Google Scholar
11.Desoer, C.A. and Vidyasagar, M., Feedback Systems: Input-Output Properties (Academic Press, New York, 1975).Google Scholar
12.Koivo, A.J. and Guo, T.H., “Adaptive Linear Controller for Robotic Manipulators” IEEE Trans. Automat. Contr. AC-28, NO. 2, 162–171 (1983).CrossRefGoogle Scholar
13.Chen, C.T., Introduction to Linear System Theory (Holt, Rinehart and Winston, New York, 1970).Google Scholar
14.You, D.S., Chung, M.J. and Bien, Z., “Real-time implementation and evaluation of dynamic control algorithms for industrial manipulators,” Proc. IECON87, SPIE 853, 26–31, Cambridge, Massachusetts, USA (11, 1987).Google Scholar