Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-10T13:07:44.832Z Has data issue: false hasContentIssue false
  • English
  • Français

An Advanced Robot Control Scheme Using ANN and Fuzzy Theory Based Solutions

Published online by Cambridge University Press:  09 March 2009

Imre J. Rudas ,
János F. Bitó  and
József K. Tar
Imre J. Rudas
Affiliation:
Department of Information Technology, Bánki Donát Polytechnic, H-1428 Budapest, P. 0. Box 31 (Hungary)
János F. Bitó
Affiliation:
Department of Information Technology, Bánki Donát Polytechnic, H-1428 Budapest, P. 0. Box 31 (Hungary)
József K. Tar
Affiliation:
Department of Information Technology, Bánki Donát Polytechnic, H-1428 Budapest, P. 0. Box 31 (Hungary)
Get access Rights & Permissions [Opens in a new window]

Summary

Due to the essential development of different means of numerical computation in the last years, new prospects have been opened for realization of different advanced control methods as conventional reasoning, fuzzy rule or ANN-based AI controls. However, it can clearly be seen, that each of these methods have significant technological limits making it expedient to seek compromises between the application of such methods and certain particular hardware solutions designed for a concrete problem. The aim of this paper is to show that in quite wide a range of practically important control tasks appropriate hardware solutions can be elaborated and combined with the above methods.

Keywords

Robot controlFuzzy theoryANN-based method.
Type
Article
Information
Robotica , Volume 14 , Issue 2 , March 1996 , pp. 189 - 198
Copyright
Copyright © Cambridge University Press 1996

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.Zadeh, L.A. “Fuzzy Sets”, Information and Control 8, 338–353 (1965).CrossRefGoogle Scholar
2.McCulloch, W.S. and Pitts, W.Bull. Math. Biophys. 5, 115–137 (1943).CrossRefGoogle Scholar
3.Lorenz, E.N. “Deterministic Non-Periodic Flow”, J. Atmospheric Sciences 20, 130 (1963).2.0.CO;2>CrossRefGoogle Scholar
4.Arnold, V.I.Mathematical Methods of Classical Mechanics (original issue in Russian by “Nauka” Moscow; Hungarian translation issued by Müszaki Könyvkiadó Budapest, Hungary, 1985).Google Scholar
5.Rudas, I.J., Kaynak, O., Bitó, J.F. & Tar, J.K.: “Robustness Analysis of a Paradigm for Non-Linear Robot Controller Based on Soft Computing Techniques” Proceedings of 20th Annual Conference of the IEEE Industrial Electronics Society (IECON'94)Bologna, Italy,5–9 September, 1994 pp. 1633–1638.Google Scholar
6.Rudas, I.J. & Bencsik, A. “Application of Linear Filtering in Industrial Robot Control” Proc. of the IEEE International Workshop on Intelligent Motion Control,Istanbul, Turkey(Aug., 1990), pp. 837–840.Google Scholar
7.Rudas, I.J., Pereszlényi, F., Tar, J.K. & Bitó, J.F.: “Unified Approach to Non-Linear Robot Control Based on RT Quasi-Diagonalization of Symmetric Matrices” Proc. of 24th Int. Symp. on Industrial Robots,Tokyo, Japan(4–6 Nov., 1993) pp. 791–798.Google Scholar
8.Press, W.H., Flannery, B.P., Teukolsky, S.A. & Vetterling, W.T.Numerical Recipes (Cambridge Univ. Press, Cambridge, 1986).Google Scholar
9.Lantos, B. “New Principles and Methods in Control and Identification of Robots” Doctoral Thesis (Hungarian Academy of Sciences (1993, in Hungarian).Google Scholar