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Neural Adaptive Switching Control of Robotic Systems

Authors: A. Denker, U. Akıncıoğlu


In this paper a neural adaptive control method has been developed and applied to robot control. Simulation results are presented to verify the effectiveness of the controller. These results show that the performance by using this controller is better than those which just use either direct inverse control or predictive control. In addition, they show that the resulting is a useful method which combines the advantages of both direct inverse control and predictive control.

Keywords: Neural networks, robotics, direct inverse control, predictive control.

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[1] Cembrano, G. and Wells, G. 1992. Neural Networks for Control, Boulberg, L. Kr─▒jgsman, A. and Vingehoods, R. A. Application of Artificial Intelligence in Process Control. Pergoman Pres, 388 - 402.
[2] Chen, L. and Narendra K. S. 2001. Nonlinear Adaptive Control Using Neural Netwoks and Multiple Models. Automatica, 1245-1255.
[3] Hunt, K. J. Sbarbaro, D. Zbikowski , R. and Gawthrop, P. J. 1992. Neural Networks for Control Systems - A Survey. Automatica, 28(6) 1083-1112
[4] Cichocki, A. Unbehauen, R. 1993. Neural Networks for Optimization and Signal Processing. WILEY. Chichester .
[5] Freeman, L. A. Skapura, D. M. 1991.Neural Networks Algorithms Applications and Programing Techniques Addison-Wesley.
[6] Noriega, J. R. and Wang, H. 1998. A Direct Adaptive Neural-Network
[7] Efe, M. Ö. ve Kaynak O. 2004. Yapay Sinir Ağları ve Uygulamaları. Boğaziçi Üniversitesi, 148s., İstanbul.
[8] Rivals, I. Personnaz, L. 2000. Nonlinear Internal Model Control sing Neural Networks: Application to Process with Delay and Design Issues, IEEE Transactions on Neural Networks, 11(1) pp 80-90.
[9] Wang, L. Wan, F. 2001. Structured Neural Networks for Constrained Model Predictive Control. Automatica, 1235-1243.
[10] Lazar, M. and Pastavanu, O. 2002. A neural predictive controller for non-linear systems, Mathematics and Computers in Simulation, 60 315- 324.
[11] Denker, A. and Ohnishi, K. 1996. Robust Tracking Control of Mechatronic Arms. IEEE/Asme Transact─▒ons on Mechatron─▒cs. 1(2), 181-188.
[12] C─▒l─▒z, M. K. 2005. Adaptive Control of Robot Manipulators with Neural Network Based Compensation of Frictional Uncertainties. Robotica, 23, 159-167.
[13] Hagan, M. T. Demuth, H. B. and Beale, M. H. 1996. Neural Network Design, University of Colorado, Colorado.