Abstract
This paper discusses the impact and significance of the PID controller and the fuzzy logic controller on the performance of a DC motor, particularly its speed regulation. On the one hand, the Proportional-Integral-Derivative (PID) controller and the fuzzy logic controller (FLC) are simulated in the MATLAB/SIMULINK environment. The simulation results, on the other hand, are validated in the experiment using LabVIEW software and a QUANSER QNET 2.0 DC motor. The LabVIEW software visualizes the system's response using virtual instruments and either stops or runs the process. A USB cable connects this software to the QUANSER QNET 2.0 DC motor. Although the PID controller is more widely used in industry, it still has some disadvantages, the most significant of which is that it cannot be more efficient with a non-linear and dynamic system. As a result, the fuzzy logic controller is presented in this work to be tested and compared to the PID controller. The fuzzy logic controller's inputs are error and change of error, and its output is armature voltage. A Mamdani engine system is used, with 7 membership functions for each input and output. The simulation and experiment results confirm that the fuzzy logic controller outperforms the PID controller in terms of stability and rapidity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Choi, J.: Modelling of DC motors: control systems lectures, pp. 1–15. Department of Mechanical Engineering, University of British Columbia (2008)
Shirazul, I., Farhad, I.B., Atif, I.: Stability analysis of a three-phase converter controlled DC motor drive. In: Third International Conference on Advanced Computing & Communication Technologies, 978–0–7695–4941–5/12 $26.00 © 2012 IEEE (2013)
Burns, R.S.: Advanced Control Engineering. Jordan Hill, Oxford (2001)
Engle, B.J., Watkins, J.M.: A Software platform for implementing digital control experiments on the QUANSER DC motor control trainer. In: Proceeding IEEE International Conference Control Applications CCA, pp. 510–515 (2008)
Quanser, N.I.: Quanser Engineering Trainer for NI-ELVIS. QNET Practical Control Guide. Quanser Inc., Canada (2009)
Brito Palma, L., Vieira Coito, F., Gomes Borracha, A. Francisco Martins, J.: A platform to support remote automation and control laboratories. In: 1st Experiment@ International Conference, Nov 17–18, Lisboa-Portugal (2011)
Nagrath, I.J., Gopal, M.: Control systems engineering, 5th edn. Delhi, India (2010)
Aisha, J., Sadi, M., Syed, O.: Controlling speed of dc motor with fuzzy controller in comparison with ANFIS controller. Intell. Control. Autom. 6, 64–74 (2015)
Ravinder, K., Vineet, G.: High performance fuzzy adaptive control for D.C. motor international archive of applied sciences and technology IAAST; vol. 3, Society of Education, India, pp. 1–10 (September 2012)
Attila, S., et al.: Fuzzy-logic controller for smart drivers. In: 9th International Conference on Information technology and Quantitative Management, Procedia Computer Science, vol. 214, pp. 1396–1403 (2022)
Bature, A.A., et al.: Design and real time implementation of fuzzy controller for DC motor position control. Int. J. Scien. Technol. Res. 2(11), 254–256 (2013)
Ramadan, E.A., El-bardini, M., Fkirin, M.A.: Design and FPGA-implementation of an improved adaptive fuzzy logic controller for DC motor speed control. Ain Shams Eng. J. 2090–4479 (2014)
Suradkar, R.P., Thosar, A.G.: Enhancing the performance of DC motor speed control using fuzzy logic. Int. J. Eng. Res. Technol. IJERT 103–110 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Meriem, M., Ahmed, G., Youness, M. (2024). PID Versus Fuzzy Logic Controller Speed Control Comparison of DC Motor Using QUANSER QNET 2.0. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-031-48465-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-48465-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48464-3
Online ISBN: 978-3-031-48465-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)