Adaptive gravitational search algorithm for PI-fuzzy controller tuning

ICINCO 2012 - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics

Volumn 1, Issue , 2012, Pages 136-141

  David, Radu Codrut ^a   Precup, Radu Emil ^a   Petriu, Emil M ^b   Rǎdac, Mircea Bogdan ^a   Purcaru, Constantin ^a   Dragoş, Claudia Adina ^a   Preitl, Stefan ^a  

^a POLITEHNICA UNIVERSITY OF TIMISOARA   (Romania)

^b UNIVERSITY OF OTTAWA   (Canada)

Author keywords

Adaptive gravitational search algorithms; Process gain sensitivity; Simulation results; Takagi sugeno PI fuzzy controllers

Indexed keywords

BEST VALUE; DIGITAL SIMULATION; GRAVITATIONAL CONSTANT; GRAVITATIONAL SEARCH ALGORITHMS; INPUT VARIABLES; INTEGRAL COMPONENTS; LOGIC BLOCKS; OPTIMAL TUNING; PI-FUZZY CONTROLLER; PROCESS GAIN; SIMULATION RESULTS; TUNING METHOD; WEIGHTED SUM;

COMPUTER SIMULATION; CONTROLLERS; FUZZY LOGIC; INFORMATION SCIENCE; ITERATIVE METHODS; LEARNING ALGORITHMS; SERVOMECHANISMS;

ROBOTICS;

EID: 84867726946     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Askari, H., Zahiri, S.-H., 2011. Decision function estimation using intelligent gravitational search algorithm. International Journal of Machine Learning and Cybernetics. DOI 10.1007/s13042-011-0052-x.
2. Åström, K. J., Hägglund, T., 1995. PID Controllers Theory: Design and Tuning. Research Triangle Park, NC: Instrument Society of America.
3. Baranyi, P., Korondi, P., Hashimoto, H., Wada, M., 1997. Fuzzy inversion and rule base reduction. In Proceedings of IEEE International Conference on Intelligent Engineering Systems (INES'97). Budapest, Hungary, 301-306.
4. Bellomo, D., Naso, D., Babuška, R., 2008. Adaptive fuzzy control of a non-linear servo-drive: theory and experimental results. Engineering Applications of Artificial Intelligence. 21, 846-857.
5. Blažič, S., Škrjanc, I., Matko, D., 2003. Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets and Systems. 139, 3-33.
6. Ferreira, P. M., Ruano, A. E., 2009. On-line slidingwindow methods for process model adaptation. IEEE Transactions on Instrumentation and Measurement. 58, 3012-3020.
7. Haber, R. E., Haber-Haber, R., Jiménez, A., Galán, R., 2009. An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process. Applied Soft Computing. 9, 889-895.
8. Hladek, D., Vaščák, J., Sinčák, P., 2009. Multi-robot control system for pursuit-evasion problem. Journal of Electrical Engineering. 60, 143-148.
9. Johanyák, Z. C., 2010. Student evaluation based on fuzzy rule interpolation. International Journal of Artificial Intelligence. 5, 37-55.
10. Juang, C.-F., Chang, P.-H., 2011. Recurrent fuzzy system design using elite-guided continuous ant colony optimization. Applied Soft Computing. 11, 2687-2697.
11. Landau, Y.-D., 1979. Adaptive Control: The Model Reference Approach. New York: Marcel Dekker.
12. Leva, A., Maggio, M., 2011. A systematic way to extend ideal PID tuning rules to the real structure. Journal of Process Control. 21, 130-136.
13. Ling, S. H., Iu, H. H. C., Chan, K. Y., Lam, H. K.., Yeung, C. W., Leung, F. H. F., 2008. Hybrid particle swarm optimization with wavelet mutation and its industrial applications. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 38, 743-763.
14. Onieva, E., Naranjo, J. E., Milanés, V., Alonso, J., García, R., Pérez, J., 2011. Automatic lateral control for unmanned vehicles via genetic algorithms. Applied Soft Computing. 11, 1303-1309.
15. Precup, R.-E., David, R.-C., Petriu, E. M., Preitl, S., Radac, M.-B., 2011a. Fuzzy control systems with reduced parametric sensitivity based on simulated annealing. IEEE Transactions on Industrial Electronics. DOI 10.1109/TIE.2011.2130493.
16. Precup, R.-E., David, R.-C., Petriu, E. M., Radac, M.-B., Preitl, S., Fodor, J., 2011b. Evolutionary optimizationbased tuning of low-cost fuzzy controllers for servo systems. Knowledge-Based Systems. DOI 10.1016/j.knosys. 2011.07.006.
17. Precup, R.-E., Preitl, S., Fur, G., 2003. PI predictive fuzzy controllers for electrical drive speed control: Methods and software for stable development. Computers in Industry. 52, 253-270.
18. Preitl, S., Precup, R.-E., 1999. An extension of tuning relations after symmetrical optimum method for PI and PID controllers. Automatica. 35, 1731-1736.
19. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S., 2009. GSA: A gravitational search algorithm. Information Sciences. 179, 2232-2248.
20. Sala, A., Guerra, T. M., Babuška, R., 2005. Perspectives of fuzzy systems and control. Fuzzy Sets and Systems. 156, 432-444.
21. Valdez, F., Melin, P., Castillo, O., 2011. An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms. Applied Soft Computing. 11, 2625-2632.
22. Visioli, A., 2004. A new design for a PID plus feedforward controller. Journal of Process Control. 14, 457-463.