DC Motor Drive Applying Conventional and Fuzzy Based PI Control Techniques

George Ch. Ioannidis, Stavros D. Kaminaris, Constantinos S. Psomopoulos, Spiros Tsiolis, Pavlos Pachos, Iraklis Villiotis, Pantelis Malatestas

Abstract


In this paper, a separately excited DC motor applying conventional PI and Fuzzy PI controller is presented. In the first, conventional PI controller, the proportional and integral gains (Kp, Ki) are estimated using a fine-tuning procedure incorporated in Matlab software while in the second one, these gains are adjusted according to Fuzzy Logic. There are 25 fuzzy rules for self-tuning of each parameter of PI controller. The Fuzzy Logic Controller has two inputs. One is the motor speed between the reference and actual speed and the second is the change in speed error (speed error derivative). Then, the outputs of the FLC namely, the parameters of the PI controller are used to control the speed of the DC Motor. The fuzzy self-tuning approach implemented on a conventional PI structure was able to improve the dynamic as well as the static response of the system. Comparison between the conventional output and the fuzzy self-tuning output was done on the basis of the simulation result obtained by Matlab/Simulink environment. The simulation results demonstrate that the designed fuzzy self-tuned PI controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with less rise and settling time, minimum overshoot, minimum steady state error and present improved performance compared to conventional PI controller.

Keywords


DC motor drive, speed control, modeling DC motors, PI control, Fuzzy PI control

Full Text:

DOC

References


Alloua, B., Gasbaoui, A.L.B., & Abderrahamani, A. (2009). Neuro-Fuzzy DC Motor speed Control Using Particle Swarm Optimization, Leonaro Electronic Journal of Practices and Technologies, 15, 1-18.

Ashraf, A.J. (2010). DC Motor Speed Control via Fuzzy / Pole Placement / PI Controller, June 2010, Master Thesis

Bansal, U.K., & Narvey, R. (2013). Speed Control of DC Motor Using Fuzzy PID Controller, Advance in Electronic and Electric Engineering, 3(9), 1209-1220,

Ioannidis, G.Ch., Psomopoulos, C.S., Kaminaris, S.D., Pachos, P., Villiotis, H., Tsiolis, S., Malatestas, P., Vokas G.A., & Manias, S.N. (2013). AC-DC & DC-DC Converters for DC Motor Drives: Review of Basic Topologies, International Conference on Electronics and Communication Systems 2013, Rhodes, Greece, pp.96-103

Ioannidis, G.Ch., Psomopoulos, C.S., Kaminaris, S.D., Vokas, G.A., Pachos, P., & Tsiolis, S. (2012). Energy Savings & Power Quality in dc Motor Drive Systems in the Hellenic Territory: Methodology & Early Results, 7th International Scientific Conference (e-RA 9), Archimedes Session, September, Egaleo, Greece, pp.65-77

Isermann, R. (1998). On Fuzzy Logic Applications for Automatic Control, Supervision, and Fault Diagnosis, IEEE Transactions on Systems, Man, and CyberneticsPart A: Systems and Humans, SMC-28(2), pp. 221-235.

Jantzen, J. (1998). Design of Fuzzy Controllers, Technical University of Denmark: Department of Automation, Technical report number 98-E 864, August, URL: http://www.iau.dtu.dk/

Lee, C.C. (1990a). Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I, IEEE Transactions on Systems, Man, and Cybernetics, 20(2), pp. 404-418.

Lee, C.C. (1990b). Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part II, IEEE Transactions on Systems, Man, and Cybernetics, 20(2), pp. 419-435.

Natsheh, E., & Buragga, K.A., (2010). Comparison between Conventional and Fuzzy Logic PID Controllers for Controlling DC Motors, IJCSI International Journal of Computer Science Issues, 7(5), 128-134.

Yager, R.R., & Filev, D.P. (1994). Essentials of Fuzzy Modeling and Control, New York: John Wiley & Sons.


Refbacks

  • There are currently no refbacks.