Trajectory Control of a Variable Loaded Servo System by using Fuzzy Iterative Learning PID Control

Author(s)

Abstract
In this study, trajectory control of the Variable Loaded Servo (VLS) system is performed by using a Fuzzy Logic based Iterative Learning Control (ILC) method. In the study, a Iterative Learning PID (IL-PID) Controller is used as the iterative learning control structure. Also, a fuzzy adjustment mechanism has been added to the control system for specify the initial parameter of the IL-PID controller. So, with combining the fuzzy logic based parameter adjustment mechanism and the IL-PID controller, Fuzzy Iterative Learning PID (Fuzzy IL-PID) controller is designed to improving the system performance. In the designed system, thanks to the fuzzy adjustment mechanism, the IL-PID controller parameters such as Kp, Ki, and Kd values are automatically adjusted to the appropriate values initially. To illustrate the effectiveness of the proposed fuzzy IL-PID controller, trajectory control of the variable loaded servo system was performed by using both Fuzzy PID and Fuzzy IL-PID control methods under the same conditions separately, and the obtained results were compared. It is seen from the results, the proposed Fuzzy IL-PID control method is to better compensate the system effect as time varying loads and has reduced the steady-state error more than other method in iterations progresses.

Keywords
Fuzzy PID control, Fuzzy IL-PID control, Trajectory control, Variable loaded servo system

Cite this paper
Omer Aydogdu, Mehmet Latif Levent, Trajectory Control of a Variable Loaded Servo System by using Fuzzy Iterative Learning PID Control , SCIREA Journal of Electrics, Communication. Volume 1, Issue 2, December 2016 | PP. 85-98.

References

[ 1 ]	Baek, S. M., Kuc, T. Y. “An adaptif PID learning Control of DC motors,” IEEE International Conference on Computational Cybernetics and Simulation, Orlando, FL, 1997, vol. 3, pp. 2877-2882. DOI: 10.1109/ICSMC.1997.635431
[ 2 ]	Zadeh, L. A. “Outline of a new approach to the analysis of complex systems and decision processes.” IEEE trans. on systems, Man and Cybernatics, vol. 3, no. 1, pp.28-44, 1973.
[ 3 ]	Elshazly, O., El-Bardini, M. “Development of self-tuning fuzzy iterative learning control for controlling a mechatronic system.” International Journal of Information and Electronics Engineering, vol. 2, no. 4, pp. 565-569, July, 2012. DOI: 10.7763/IJIEE.2012.V2.162
[ 4 ]	Pok, Y. M. Liew, K. H. and Xu, J. X. “Fuzzy PD iterative learning control algorithm for improving tracking accuracy,” IEEE International Conference on Systems, Man and Cybernatics, San Diego, CA, 1998, vol. 2, pp. 1603-1608. DOI: 10.1109/ICSMC.1998.728117.
[ 5 ]	Wei, J. “Adaptive iterative learning control for a class of nonlinear time-varying systems with unknown delays and input dead-zone.” IEEE/CAA Journal of Automatica Sinica, vol. 1, no. 3, pp. 302-314, July, 2014. DOI: 10.1109/JAS.2014.7004688.
[ 6 ]	Hu, C. R., Lin, S. S. Sheng, H. Z. “A new discrete-time adaptive ILC for nonlinear systems with time-varying parametric uncertainties.” Acta Automatica Sinica, vol. 34, no. 7, pp. 805-808. 2008 (in Chinese). DOI: 10.3724/SP.J.1004.2008.00805.
[ 7 ]	Alkan, O., Aydogdu, O. “Fuzzy model reference learning control of a time-varying rotary servo systems,” Proceedings of Second International Conference on Informatics, Çanakkale, (ICI'2011), 2011, pp. 1-7.
[ 8 ]	Kasnakoglu, C., “Modeling and control of flow problems by adaptation-based linear parameter varying models.” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 18, 2010. DOI:10.3906/elk-0906-5.
[ 9 ]	Fadil, M.A., “PID Controller for Micro-Unmanned Air Vehicle (Micro-UAV).” BS thesis, Mekanikal-Aeronautik, Universiti Teknologi, Kuala Lumpur, Malaysia, 2012.
[ 10 ]	Precup, R. E., Preitl, S., Petriu, E. M., Tar, J. K. and Fodor, J. “Iterative learning-based fuzzy control system,” in IEEE International Workshop on Robotic and Sensors Environments, Ottawa, Canada, 2008, pp. 25-28.
[ 11 ]	Dotoli, M., Maione, B. and Turchiano, B. “Fuzzy-Supervised PID Control: Experimental Results,” in 1st European Symposium on Intelligent Technologies, Tenerife, Spain, EUNITE 2001, pp. 31–35.
[ 12 ]	Feng, G. “A Survey on Analysis and Design of Model-Based Fuzzy Control Systems.” IEEE Trans. on Fuzzy Sys., vol. 14, no. 5, pp. 676–697, October, 2006. DOI: 10.1109/TFUZZ.2006.883415
[ 13 ]	Lee, C. C. “Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I.” IEEE Transactions on Systems, Man and Cybernetics, vol. 20, no. 2, pp. 404–418, March/April, 1990.
[ 14 ]	Ahn, H. S., Chen, Y. Q. and Moore, K. L. “Iterative Learning Control: Brief Survey and Categorization.” IEEE Transactions On Systems, Man, And Cybernetics, Part C: Applications And Reviews, vol. 37, no. 6, pp 1099-1121, November, 2007. DOI: 10.1109/TSMCC.2007.905759
[ 15 ]	Arimoto, S., Kawamura, S., and Miyazaki, F. “Bettering operation of dynamic systems by learning: A new control theory for servomechanism and mechatronics systems,” The 23rd IEEE Conference on Decision and Control, Las Vegas, NV, 1984, pp. 1064-1063. DOI: 10.1109/CDC.1984.272176
[ 16 ]	Fadil, M. A., Jalil N. A., and Mat Darus, I. Z. “Intelligent PID controller using iterative learning algorithm for active vibration controller of flexible beam,” IEEE Symposium on Computers & Informatics, Langkawi, ISCI, 2013, pp. 80-85. DOI: 10.1109/ISCI.2013.6612380.