The International Arab Journal of Information Technology (IAJIT)


A Hierarchical Neuro-Fuzzy MRAC of a Robot in Flexible Manufacturing Environment

In one hand, the Model Reference Adaptive Control (MRAC) architecture has been widely used in linear adaptive control field. The control objective is to adjust the control signal in a stable manner so that the plant’s output asymptotically tracks the reference model’s output. The performance will depend on the choice of a suitable reference model and the derivation of an appropriate learning scheme. While in the other hand, clusters analysis has been employed for many years in the field of pattern recognition and image processing. To be used in control the aim is being to find natural groupings among a set of collected data. The mean-tracking clustering algorithm is going to be used in order to extract the input-output pattern of rules from applying the suggested control scheme. These rules will be learnt later using the widely used Multi-layer perceptron neural network to gain all the benefits offered by those nets. A hierarchical neuro-fuzzy MRAC is suggested to control robots in a flexible manufacturing system. This proposed controller will be judged for different simulated cases of study to demonstrate its capability in dealing with such a system.


[1] Al-Titinchi A. A. and Al-Aubidy M. K., “A Hierarchical Manufacturing Route Planner Design Based on Heuristic Algorithm: Design & Evaluation,” Systems Analysis Modelling Simulation Magazine, vol. 42, no. 7, pp. 1119- 1141, July 2002. Output(t) 214 The International Arab Journal of Information Technology, Vol. 1, No. 2, July 2004

[2] Al-Titinchi A. A. and Al-Aubidy M. K., “Modeling an Interactive FMS Scheduler Using Colored Petri Nets,” in Proceeding of 2nd Middle East Conference on Simulation and Modeling (SCS-MESM’2000), Jordan, pp. 54-61, 2000.

[3] Astrom K. J. and Wittenmark B., Adaptive Control, Addison Wesley, Reading, MA, 1989.

[4] Brown M. and Harris C., Neuro-Fuzzy Adaptive Modeling and Control, Prentice-Hall International Ltd., UK, 1994.

[5] Ge S. S., Lee T. H., Gu D. L., and Woon L. C., “A One-Step Solution in Robotic Control System Design,” IEEE Robotics and Automation, vol. 7, no. 3, pp. 42-54, 2000.

[6] Jamshidi M., Vadiee N., Rose J. T., and Ross T., Hardware Applications of Fuzzy Logic Control, In Soft Computing: Fuzzy Logic, Neural Networks, and Distributed Artificial Intelligence, in Aminzadeh F. and Jamshidi M. (Eds), Chapter 3, Prentice-Hall, USA, 1994.

[7] Kim S. W. and Lee J. J., “Neural Network Control by Learning the Inverse Dynamics of Uncertain Robotic System,” Journal of Institute of Control, Automatic System Engineering, vol. 1, no. 2, pp. 88-93, 1995.

[8] Li Q., Poo A. N., Teo C. L., and Lim C. M., “Developing a Neurocompensator for the Adaptive Control of Robots,” in Proceedings of IEE, Control Theory Applications, vol. 142, no. 6, pp. 562-568, 1995.

[9] Linkens D. A. and Nie J., “Constructing Rule- Bases for Multivariable Fuzzy Control by Self- Learning- Part 2,” International Journal of Systems SCI, vol. 24, no. 1, pp. 129-157, 1993.

[10] Liu M., “An Adaptive Control Scheme for Robotic Manipulator,” in Proceedings of 5th International Symposium on Industrial Robotics, USA, 1985.

[11] Palm R., “Fuzzy Controller for Sensor Guided Robot Manipulator,” Fuzzy Sets and Systems, USA, 1989.

[12] Psalits D., Sideris A., and Yamamura A. A., “Neural Controllers,” in Proceedings of IEEE 1st International Conference on Neural Networks, San Diego, CA, vol. 4, pp. 551-558, 1987.

[13] Spong W., Robot Dynamics & Control, John Wiley & Sons, USA, 1989.

[14] Su C. Y. and Leung T. P., “A Sliding Mode Controller with Bound Estimation for Robot Manipulators,” IEEE Transactions Robotics & Automations, vol. 9, no. 2, pp. 208-214, 1993.

[15] Tsai C. H., Wang C. H. , and Lin W. S., “Robust Fuzzy Model Following Control of Robot Manipulators,” IEEE Transactions Fuzzy Systems, vol. 8, no. 4, pp. 462-469, 2000.

[16] Viswanadhan N. and Narahari Y., “Performance Modeling of Automated Manufacturing Systems,” Prentice-Hall, USA, 1994.

[17] Voda A. A. and Landau I. D., “A Method for the Auto-Calibration of PID Controllers,” Automatica, vol. 31, no. 1, pp. 41-55, 1995.

[18] Warwick K., “New Ideas in Fuzzy Clustering and Fuzzy Automata,” in Proceeding of the International ICSC Symposium on Fuzzy Logic, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, May 26-27, 1995. Kasim Al-Aubidy received his BSc and MSc degrees in control and computer engineering from the University of Technology, Iraq in 1979 and 1982, respectively, and the PhD degree in real-time computing from the University of Liverpool, England in 1989. He is currently an associative professor in the Department of Computer Engineering at Philadelphia University, Jordan. His research interests include fuzzy logic, neural networks, genetic algorithm and their real-time applications. He is the winner of Philadelphia Award for the best researcher in 2000. He is also a member of the editorial board of the Asian Journal of Information Technology. He has coauthored 3 books and published 47 papers on topics related to computer applications. Mohammed Ali received his BSc, MSc and PhD degrees in computer and control engineering from the University of Technology, Iraq in 1981, 1993, and 1998, respectively. He is currently an assistant professor in the Department of Computer Engineering at Philadelphia University, Jordan. His research interests include fuzzy logic, neural networks, image processing, and computer interfacing. He has 6 published papers related to neurofuzzy and real-time computer control applications.