Neuro-fuzzy control of a mobile robot

Neurocomputing

Volumn 28, Issue 1-3, 1999, Pages 127-143

  Godjevac, Jelena ^a   Steele, Nigel ^b  

^a EPFL   (Switzerland)

^b Institute for Clean Growth and Future Mobility   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROL SYSTEM; CYBERNETICS; PRIORITY JOURNAL; ROBOTICS;

EID: 0032857738     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(98)00119-2     Document Type: Article

References (19)

1. K.J. Astrom, P. Eykhoff, System identification - a survey, Automatica 7 (1971) 123-162.
2. A. Basso, F. Mondada, C. Castelfranchi, Reactive goal activation in intelligent autonomous agent architectures, AAI93 Symposium on Abstract Intelligent Agents, Rome, January 1993.
3. V. Braitenberg, Vehicles, MIT, Cambridge, MA, 1984.
4. R. Brooks, A robust layered control system for a mobile robot, IEEE Robotics Automat. RA-2 (1986) 14-23.
5. D. Floreano, F. Mondada, Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot, in: SAB94, Brighton, 1994.
6. P. Gaussier, S. Zrehen, A topological neural map for on-line learning: emergence of obstacle avoidance, Proceedings of SAB, from Animals to Animals 94, Brighton, 1994, pp. 182-190.
7. J. Godjevac, A learning procedure for a fuzzy system: application to obstacle avoidance, Proceedings ICSC Symposium on Fuzzy Logic (ISFL95), Zuerich, Switzerland, May 1995, pp. C142-148.
8. J. Godjevac, Comparative study of fuzzy control, neural network control and neuro-fuzzy control, in: D. Ruan (Ed.), Fuzzy Set Theory and Advanced Mathematical Applications, Kluwer Academic, Dordrecht, June 1995, pp. 291-322 (Chapter 12).
9. J.-S.R. Jang, ANFIS, Adaptive-network-based fuzzy inference systems, IEEE Trans. Systems Man Cybernet. 23 (3) (1992) 665-685.
10. L. Ljung, T. Soderstrom, Theory and Practice of Recursive Identification, Prentice-Hall Signal Processing Series, MIT, Cambridge, MA, 1983.
11. F. Mondada, E. Franzi, P. Ienne, Mobile robot miniaturization: a tool for investigation in control algorithms, Informatik (1994) 17-20.
12. H. Nomura, I. Hayashi, N. Wakami, A learning method of fuzzy inference rules by descent method, Proceedings of IEEE International Conference on Fuzzy Systems, San Diego, 1992, pp. 203-210.
13. H. Robbins, S. Monro, A stochastic approximation method, Ann. Math. Statist. 22 (1951) 400-407.
14. N.C. Steele, A. Dobnikar, Novel internal units for neural network based fuzzy adaptive inference systems, Proceedings of the Twenty fourth Euromicro Conference Vasterås, Sweden, IEEE, August 1998, pp. 647-654.
15. N. Steele, P. King, S. Poopalasingham, Adaptive radial basis function neural networks applied to fuzzy control, Proceedings ICSC Symposium on Fuzzy Logic (ISFL95), Zuerich, Switzerland, May 1995, pp. C80-C85.
16. T. Takagi, M. Sugeno, Derivation of fuzzy control rules from human operator's control actions, Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, July 1983, pp. 55-60.
17. P.F.M.J. Verschure, B.J.A. Koese, R. Pfeifer, Distributed adaptive control: the self-organization of structured behavior, Robotics Autonomous Agents 9 (1992) 181-196.
18. B. Widrow, S.D. Stearns, Adaptive Signal Processing, Prentice-Hall Signal Processing Series, Prentice-Hall, Englewood Cliffs, NJ, 1985.
19. S. Zrehen, P. Gaussier, Why topological maps are useful for learning in an autonomous agent, Proceedings PerAc, Lausanne, IEEE Press, New York, September 1994.