Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Genetic-Fuzzy Inverse Controller for a Robot Arm Suitable for On Line Applications
Authors: Abduladheem A. Ali, Easa A. Abd
Abstract:
The robot is a repeated task plant. The control of such a plant under parameter variations and load disturbances is one of the important problems. The aim of this work is to design Geno-Fuzzy controller suitable for online applications to control single link rigid robot arm plant. The genetic-fuzzy online controller (indirect controller) has two genetic-fuzzy blocks, the first as controller, the second as identifier. The identification method is based on inverse identification technique. The proposed controller it tested in normal and load disturbance conditions.Keywords: Fuzzy network, genetic algorithm, robot control, online genetic control, parameter identification.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072060
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1458References:
[1] K. J. Astrom, B. Wittenmark, "Adaptive Control", Addison Wesley, 1989.
[2] T. L. Seng, M. Khalid, R. Yusof, S. Omatu, "Adaptive Neuro-Fuzzy Control System by RBF and GRNN Neural Networks", Intelligent and Robotic System Journal, vol. 23, PP.267-289, 1998.
[3] K. M. Passino, S. Yurkovich, "Fuzzy Control", Addison Wesley Longman, Inc., 1998.
[4] J. G. Kuschewski, S. Hui, S. H. Zak, "Application of Feedforward Neural Networks to Dynamical System Identification and Control", IEEE Transaction on Control System Technology, vol.1, No.1, March 1993.
[5] J. N. Abdulbaqi, "Neuro-Fuzzy Control of Robot Arm", M.Sc. Thesis, Department of Electrical Engineering, Basrah University, 2004.
[6] J. M. Herrero, X. Blasco, M. Martinez, J. V. Salcedo, "Optimal PID Tuning with Genetic Algorithms for Nonlinear Process Models", 15th Triennial World Congress, Barcelona, Spain, 2002.
[7] M. Mitchell, "An Introduction to Genetic Algorithms", Aisradford Book, The MIT press, Cambridge, Massachusetts, London, England, 1998.
[8] H. A. Younis, "Attacking Stream Cipher Systems Using Genetic Algorithm", M.Sc. Thesis, Department of Computer Science, Basrah University, 2000.
[9] M. Schmidt, T. Stidsen, "Hybrid Systems: Genetic Algorithms, Neural Networks, and Fuzzy Logic", Aarhus University, Denmark, 1996.
[10] G. Lightbody, G. W. Irwin, "Nonlinear Control Structures Based on Embedded Neural System Models", IEEE Transactions on Neural Networks, vol.8, No.3, May 1997.
[11] M. S. Ahmed, "Neural-Net-Based Direct Adaptive Control for a Class of Nonlinear Plants", IEEE Transactions on Automatic Control, vol.45, No.1, January 2000.