Modeling and Optimal Control of a DC Motor

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-32 Number-3
Year of Publication : 2016
Authors : Tayfun Abut
DOI :  10.14445/22315381/IJETT-V32P227


Tayfun Abut "Modeling and Optimal Control of a DC Motor", International Journal of Engineering Trends and Technology (IJETT), V32(3),146-150 February 2016. ISSN:2231-5381. published by seventh sense research group

Realization in the simulation environment in order to acquire the behaviors of the systems prior to the real-time studies is of great importance in terms of detecting the faults that may occur during the real-time studies and distinguishing them in the algorithm developing phases and preventing them. In this study; modeling, inspecting, and the effect of the kalman filter in the noise environment and comparing the performances of the filtered controllers of a direct current (DC) motor is shown. Linear quadratic regulator (linear quadratic regulator-LQR) and proportional integral differential (proportional-integral-derivative PID) methods among the optimal control techniques were used for the speed monitoring of the DC motor. Both process noise and computation noise were applied to the DC motor system. Kalman filter was designed to increase the performance of the controllers in the noise environment. The controller was designed in the MATLAB environment and according to the gathered simulation environment results, the applied control methods were compared and the results were scrutinized.


[1] Madani, T., & Benallegue, A. (2006, December). Control of a quadrotor mini-helicopter via full state backstepping technique. In Decision and Control, 2006 45th IEEE Conference on (pp. 1515-1520). IEEE.
[2] Yeung, K., & Huang, J. (2003). Development of a remoteaccess laboratory: a dc motor control experiment. Computers in Industry, 52(3), 305-311.
[3] Praveen, R. P., Ravichandran, M. H., Achari, V. S., Raj, V. J., Madhu, G., & Bindu, G. R. (2012). A novel slotless Halbach-array permanent-magnet brushless dc motor for spacecraft applications. Industrial Electronics, IEEE Transactions on, 59(9), 3553-3560.
[4] Li, W., Xu, D., Zhang, W., & Ma, H. (2007, May). Research on wind turbine emulation based on DC motor. In Industrial Electronics and Applications, 2007. ICIEA 2007. 2nd IEEE Conference on (pp. 2589-2593). IEEE.
[5] Aung, W. P. (2007). Analysis on modeling and simulink of DC motor and its driving system used for wheeled mobile robot. World Academy of Science, Engineering and Technology, 32, 299-306.
[6] Ohishi, K., Nakao, M., Ohnishi, K., & Miyachi, K. (1987). Microprocessor-controlled DC motor for load-insensitive position servo system. IEEE Transactions on Industrial Electronics, (1), 44-49.
[7] Ahmed, A. M., Ali-Eldin, A., Elksasy, M. S., & Areed, F. F. (2015). Brushless DC Motor Speed Control using both PI Controller and Fuzzy PI Controller. International Journal of Computer Applications, 109(10), 29-35.
[8] N. Praboo and P. Bhaba, "Simulation work on Fractional Order PI Control Strategy for Speed Control of DC Motor Based on Stability Boundary Locus Method", International Journal of Engineering Trends and Technology (IJETT), Volume 4, Issue 8, 2013.
[9] Devender Kumar, Ishan Thakur, Kanika Gupta "Direct Torque Control for Induction Motor using Intelligent Artificial Neural Network Technique" International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 3, Issue 4, July-August 2014, pp:44- 50.
[10] Hameed, W. I., Kadhim, A. S., & Al-Thuwaynee, A. A. K. (2016). Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm. Engineering, 8(01), 1.
[11] Kassem, A.M.; Yousef, A. M. (2009). Servo DC Motor Position Control Based on Sliding Mode Approach, 5th Saudi Technical Conference and Exhibition STCEX 2009, January 2009
[12] Gaddam Mallesham and Akula Rajani ,”AUTOMATIC TUNING OF PID CONTROLLER USING FUZZY LOGIC .” 8th International Conference on DEVELOPMENT AND APPLICATION SYSTEMS Suceava, Romania, May 25 – 27, 2006.
[13] Wang Xiao-kan, Sun Zhong-liang, Wanglei, Feng Dongqing, "Design and Research Based on Fuzzy PIDParameters Self-Tuning Controller with MATLAB," Advanced Computer Theory and Engineering, International Conference on, pp. 996-999, 2008 International Conference on Advanced Computer Theory and Engineering, 2008.
[14] Abut, T. "Dynamic Model and Optimal Control of A Snake Robot: TAROBOT–1." International Journal of Scientific & Technology Research. Volume 4, Issue 11/2015; 4(11):240-248.
[15] S.Weerasoorya and M.A Al-Sharkawi “Identification andcontrol of a DC Motor using back-propagation neuralnetworks” IEEE transactions on Energy Conversion, Vol.6,No.4 pp663-669, December 1991.
[16] El-Gammal, A. A., & El-Samahy, A. A. Optimal Tuning of PID Controller on a DC Motor Drives Using Particle Swarm Optimization PSO. Al-Azhar University Engineering international scientific Journal (JAUES), ISSN, 1110-6409.
[17] Meshram, P.M. ve Kanojiya, R.G., “Tunnig of PID Controller using Ziegler-Nichols Method for Speed Control of DC Motor”, International Conference On Advances in Engineering and Management (ICAESM), s:117-122, 2012
[18] Ogata K., "Modern control Engineering". New Jersey, Prentice-Hall, 1990.
[19] Astrom K. J. and Hagglund T.., "PID controllers: theory, design and tuning". Instrument Society of America, 2nd edition, 1995.
[20] Ziegler, J.B., and Nichols, N.B. (1942), The classic original paper: "Optimum settings for automatic controllers," ASME Transactions, v64 , pp. 759-768.
[21] B. D. Anderson and J. B. Moore, “Optimal Control: Linear Quadratic Methods,” Prentice Hall, New Jersey, 1990.
[22] Nazarathy, Y., Pulemotov, A. (2012). MATH4406 (Control Theory) Unit 6: The Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC), September 12.
[23] Nenad Muskinja, Boris Tovornik. “Swing up and stabilization of a real inverted pendulum”. IEEE Trans on Industrial Electronics, 2006, vol. 53:2,18. pp. 631-639.
[24] S. Franko, “LQR-Based Trajectory Control Of Full Envelope, Autonomous Helicopter,” Proceedings of the World Congress on Engineering 2009, London, 1-3 July 2009.
[25] Kalman, R. E., “Contributions to the Theory of Optimal Control,” Bol. Soc Mat. Mex., 5 (1960), pp. 102–19. [26] Mohinder S G, Angus P A. “Kalman filtering: Theory and practice”. 2nded. New York: John Wiley and Sons, 2001, pp. 133-148.
[27] C. Yongyai, A. Shimada, and N. Fujii: Kalman filter based disturbance observer and an observation to the use of disturbance estimate, Proc. 51st Joint Automatic Control Conference, pp.1226–1231, 2008 (in Japanese).

Kalman Filter, LQR, PID, DC Motor