Design of Optimal PID, Fuzzy and New Fuzzy-PID Controller for CANSAT Carrier System Thrust Vector

Subject Areas : Mechanical Engineering

A. Kosari ¹ , H. Jahanshahi ² , A. A. Razavi ³

1 - Department of New Sciences and Technologies, University of Tehran, Iran
2 - Department of New Sciences and Technologies, University of Tehran, Iran
3 - Department of New Sciences and Technologies, University of Tehran, Iran

Received: 2015-02-04 Accepted : 2015-05-20 Published : 2015-06-01

Keywords: CANSAT carrier system, PID-controller, Fuzzy-PID controller, Genetic Algorithm, Multi-objective optimization, Fuzzy controller,

Abstract :

In this paper, multi-objective optimization based on Genetic Algorithm is used to find the design variables of PID, fuzzy and new Fuzzy-PID controllers applying for a thrust vector control of CANSAT carrier system. Motion vector control is considered according to the dynamic governing equation of the system which is derived using Newton’s method and defined mission in delivering payload into the specific height and flight path angle. The cost functions of the system are position error from the set point and deviation of the vector angle of carrier system with carrier body, where these cost functions must be minimized simultaneously. Results demonstrate that this new Fuzzy-PID controller is superior to other controllers which are exerted in the thrust vector control of a CANSAT carrier system. This Fuzzy-PID is capable of doing the mission with decrease in settling time and rise time with respect to the convenient minimized objective function values.

References:

[1]      Aydemir, M. E., Dursun, R. C., and Pehlevan, M., “Ground Station Design Procedures for CANSAT”, the 6th International Conference on Recent Advanced in Space Technologies (RAST), Istanbul, Turkey, June 2013, pp. 909-912.

[2]      Soyer, S., “Small Space Can: CANSAT,” in 5th International Conference on Recent Advanced in Space Technologies (RAST), Istanbul, Turkey, June 2011, pp. 789-793.

[3]     Çabuloğlu, C., Aykiş, H., Yapacak, R., Çalişkan, E., Ağirbuş, Ö., Abur, Ş., Soyer, S., Türkmen, H., Ay, S., Karataş, Y., Aydemir, M. E., and Ҫelebi, M., “Mission Analysis and Planning of a CANSAT”, The 5th International Conference on Recent Advanced in Space Technologies (RAST), Istanbul, Turkey, June 2011, pp. 794-799.

[4]      Okninski, A., Marciniak, B., Bartkowiak, B., Kaniewski, D., Matyszewski, J., Kindracki, J., and Wolanski, P., “Development of the Polish Small Sounding Rocket Program”, Acta Astronautica, Vol. 108, 2015, pp. 46-56.

[5]      Zadeh, L. A., “Fuzzy algorithms”, Information and Control, Vol. 12, 1968, pp. 94-102.

[6]      Zadeh, L. A., “Outline of a new approach to the analysis of complex systems and decision processes”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 3, 1973, pp. 28-44.

[7]      Nasser, H., Kiefer-Kamal, E. H., Hu, H., Belouettar, S., and Barkanov, E., “Active vibration damping of composite structures using a nonlinear fuzzy controller”, Composite Structures, Vol. 94, 2012, pp. 1385-1390.

[8]      LI, P., JIN, F. J., “Adaptive Fuzzy Control for Unknown Nonlinear Systems with Perturbed Dead-zone Inputs”, Acta Automatica Sinica, Vol. 36, 2010, pp. 573-579.

[9]      Lygouras, J. N., Botsaris, P. N., Vourvoulakis, J., and Kodogiannis, V., “Fuzzy logic controller implementation for a solar air-conditioning system”, Applied Energy, Vol. 84, 2007, pp. 1305-1318.

[10]   Jee, S., Koren, Y., “Adaptive fuzzy logic controller for feed drives of a CNC machine tool”, Mechatronics, Vol. 14, 2004, pp. 299-326.

[11]   Sinha, A. S. C., Lyshevski, S., “Fuzzy control with random delays using invariant cones and its application to control of energy processes in microelectromechanical motion devices”, Energy Conversion and Management, Vol. 46, 2005, pp. 1305–1318.

[12]   Zuperl, U., Cus, F., and Milfelner, M., “Fuzzy control strategy for an adaptive force control in end-milling”, Journal of Materials Processing Technology, Vol. 164-165, 2005, pp. 1472–1478.

[13]   Mansour, S. E., Kember, G. C., Dubay, R., and Robertson, B., “Online optimization of fuzzy-PID control of a thermal process”, ISA Transactions, Vol. 44, 2005, pp. 305-314.

[14]   Duan, X. G., Li, H. X., and Deng, H., “Robustness of fuzzy PID controller due to its inherent saturation”, Journal of Process Control, Vol. 22, 2012, pp. 470-476.

[15]   Oh, S. K., Jang, H. J., and Pedrycz, W., “Optimized fuzzy PD cascade controller: A comparative analysis and design”, Simulation Modelling Practice and Theory, Vol. 19, 2011, pp. 181-195.

[16]   Karasakal, O., Guzelkaya, M., Eksin, I., Yesil, E., and Kumbasar, T., “Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration”, Engineering Applications of Artificial Intelligence, Vol. 26, 2013, pp. 184-197.

[17]   Boubertakh, H., Tadjine, M., Glorennec, P. Y., and Labiod, S., “Tuning fuzzy PD and PI controllers using reinforcement learning”, ISA Transactions, Vol. 49, 2010, pp. 543-551.

[18]   Nie, M., Tan, W. W., “Stable adaptive fuzzy PD plus PI controller for nonlinear uncertain systems”, Fuzzy Sets and Systems, Vol. 179, 2011, pp. 1-19.