Applying RBF Neural Nets for Position Control of an Inter/Scara Robot

Authors

  • Fernando Passold Pontifical Catholic University of Valparaí­so College of Electrical Engineering Avenida Brasil 2147, Valparaí­so, Chile

Keywords:

manipulator robots, position-force control, neural networks

Abstract

This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on the feedback error learning architecture. The main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained on-line have been used, without requiring any previous knowledge about the system to be controlled. These approach has performed very successfully, with better results obtained with the RBF networks when compared to PID and sliding mode positional controllers.

References

Sciavicco, L. and Siciliano, B., Modeling and Control of Robot Manipulators, McGraw-Hill, 1996.

Narendra, K.S., Neural networks for real-time control. In 36th IEEE Conference on Decision and Control - CDC'97, 1026-1031, San Diego, California, USA, 1997. http://dx.doi.org/10.1109/CDC.1997.657581

Katiˇc, D. and Vukobratovi ˇc, M., Connectionist based robot control: an overview. In 13th IFAC, volume 1b-05 6, 169-174. San Francisco, USA, 1996.

Morris, A.S. and Khemaissia, S., Artificial neural network based intelligent robot dynamic control. In A.M.S. Zalzala and A.S. Morris (eds.), Neural Networks for Robotic Control - Theory and Applications, chapter 2, 26-63, Ellis Horwood, Great Britain. 1996.

Kim, Y.H. and Lewis, F.L., Neural network output feedback control of robot manipulators. IEEE Transaction on Robotics and Automation, 15(2), 301-309, 1999. http://dx.doi.org/10.1109/70.760351

Haykin, S., Neural Networks A Comprehensive Foundation, Prentice Hall, New Jersey, USA, 2nd edition, 1999.

Gabrijel, I. and Dobnikar, A., Adaptative RBF neural network. In SOCO'97 Conference, 164-170. Nimes, France. URL http://cherry.fer.uni-lj.si:80/»gabriel/soco97/soco97.zip, 1997.

Girosi, F. and Poggio, T., Networks and the best approximation property. In M.M. Gupta and D.H. Rao (eds.), Neuro-Control Systems, Theory and Applications, 257-264. IEEE Pres, Piscataway, New Jersey, USA, 1993.

Fritzke, B., Incremental neuro-fuzzy systems. In Applications of soft computing, SPIE International Symposium on Optical Science, Engineering and Instrumentation, San Diego, 1997.

Kiguchi, K. and Fukuda, T., Intelligent position/force controller for industrial robot manipulators - application of fuzzy neural networks, IEEE Transactions on Industrial Electronics, 44(6), 753-761, 1997. http://dx.doi.org/10.1109/41.649935

Published

2009-06-01

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