Fast Self-organizing Fuzzy Control of Nonlinear High-order Systems

Title

Fast Self-organizing Fuzzy Control of Nonlinear High-order Systems

Abstract

In this research, a self-learning fuzzy logic controller (SLFLC) is described whose learning algorithm utilizes a second-order reference model and a sensitivity model. The proposed controller has been tested in the position control loop of a dc servo system affected by a backlash nonlinearity. The simulation results have proved that the SLFLC provides a desired closed-loop behavior and also significantly reduces a steady-state position error caused due to the presence of the nonlinearity.

Finance

FANUC FA and Robot Foundation, Fanuc Ltd., Japan

Year

1996

Principal investigators

Zdenko Kovacic, Stjepan Bogdan

Project references:

Z. Kovacic, S. Bogdan, “Fast Self-organizing Fuzzy Control of Nonlinear High-Order Systems”, Reports on Researches and Developments assisted in fiscal year 1995, Foundation for Promotion of Advanced Automation Technology c/o Fanuc Ltd., pp.27-30, January 1998.

Z. Kovacic, S. Bogdan, T. Reichenbach, “Nonlinear Position Control by Using Multiple Position-dependent Self- organizing Fuzzy Logic Controllers”, The 6th IFAC-Symposium on Robot Control SYROCO ´00, pp.229-233 , Vienna, Austria, 2000.

Z. Kovacic, S. Bogdan, T. Reichenbach, “Demonstration of Self-learning Fuzzy Logic Controller Performance in the Matlab+SimulinkTM Environment”, CD-ROM Proceedings of The 8th IEEE Mediterranean Conference on Control & Automation MED 2000, TC-2.5, Patras, Greece, 2000.

Z. Kovacic, S. Bogdan, M. Balenovic, “A Model Reference & Sensitivity Model-based Self-learning Fuzzy Logic Controller as a Solution for Control of Nonlinear Servo Systems”, The IEEE Transactions on Energy Conversion, Vol. 13, No.4, pp. 1479-1484, December 1999.

N.E. Mastorakis (editor): Computational Intelligence and Applications, World Scientific and Engineering Society Press, Z. Kovacic, V. Petik, T. Reichenbach, S. Bogdan: “Robust Self-Learning Fuzzy Logic Servo Control with Neural Network-Based Load Compensator”, pp. 175-180, ISBN: 960-8052-05-X, 1999.

Z. Kovacic, V. Petik, T. Reichenbach, S. Bogdan, “Robust Self-Learning Fuzzy Logic Servo Control with Neural Network-Based Load Compensator”, CD-ROM Proceedings of The 3rd IMACS/IEEE International Conference on Circuits, Systems Communications and Computers, Athens, 1999.

Z. Kovacic, M. Balenovic, S. Bogdan, “Sensitivity-based Self-learning Fuzzy Logic Control for a Servo System”, The IEEE Control Systems Magazine, Vol. 18, No.3, pp. 41-51, June, 1998.

Z. Kovacic, S. Bogdan, M. Balenovic, “Robustness Improvement of a Model Reference & Sensitivity Model-based Self-learning Fuzzy Logic Controller”, Proc. of the 1998. IEEE Conference on Control Applications, pp. 643-647, Trieste, 1998.

Z. Kovacic, S. Bogdan, M.Balenovic, “A Sensitivity-Based Self-Learning Fuzzy Logic Controller as a Solution for a Backlash Problem in a Servo System”, Proceedings of The 1997 IEEE International Electric Machines and Drives Conference IEMDC’97, pp. TC2-11.1-TC2-11.3, Milwaukee, 1997.

Z. Kovacic, M. Balenovic, S. Bogdan, “An Experimental Verification of a Model Reference and Sensitivity Model-based Self-learning Fuzzy Logic Controller Applied to a Nonlinear Servosystem”, Proc. of the 12th IEEE International Symposium on Intelligent Control, pp. 263-268, Istanbul, 1997.

S.Bogdan, Z. Kovacic, “On the Design of Self-Learning Fuzzy Controllers for Nonlinear Control Systems by Using a Reference Model and a Sensitivity Model”, Proceedings of the 4th IEEE Mediterranean Symposium on Control&Automation, pp. 799-804, Chania (Crete), 1996.