CONTROLLING WATER LEVEL BY USING MODIFIED MODEL FREE ADAPTIVE CONTROLLER

Authors

  • Yousra Abd Mohammed Department of Communication Engineering, Technology University, Baghdad, Iraq Author https://orcid.org/0000-0002-6432-0754
  • Ekhlas H. Karam Computer Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq Author
  • Nahida N. Kadhim Department of Control and System Engineering, Technology University, Baghdad, Iraq Author

DOI:

https://doi.org/10.31272/jeasd.27.3.7

Keywords:

Water level system, DC motor, model free adaptive controller, optimization algorithm, tuning methods

Abstract

This paper investigates a simple mathematical model for a water level system, which consists of a DC motor (water pump), and a Speed to Height transformation block, that relates the speed of the motor, to the height of the water level. The input signal is the applied voltage to the armature of the DC motor, while the output signal is the rotational speed of the shaft.  A simple modified model-free adaptive controller is suggested, to control the level of water, by adjusting the rate of the incoming water flow to the container, by changing the speed of the water pump, that fills the container. The suggested controller consists of a conventional model free adaptive controller, combined with the proportional integral derivative controller. The parameters of the controller are tuned using two methods. The overall controlled water level system is simulated through MATLAB R2015a software. The results show the efficiency of the suggested controller, when compared to the tuned PID and the MFAC, due to its least fluctuation peak, fast response with a small settling time, and zero steady-state error.

References

Niimura, T., & Yokoyama, R., (1995). Water level control of small-scale hydro-generating units by fuzzy logic. In 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century. Vol. 3, pp. 2483-2487. https://doi.org/10.1109/icsmc.1995.538154.

Roubos, J. A., Babuska, R., Bruijn, P. M., & Verbruggen, H. B., (1998). Predictive control by local linearization of a Takagi-Sugeno fuzzy model. In 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98CH36228). Vol. 1, pp. 37-42 https://doi.org/10.1109/FUZZY.1998.6874.

Getu, B. N., (2016). Water Level Controlling System Using Pid Controller. International Journal of Applied Engineering Research ISSN 0973-4562 Vol. 11, No. 23, pp. 11223-11227.

Pratama, S. C., Susanto, E., & Wibowo, A. S., (2016). Design and implementation of water level control using gain scheduling PID back calculation integrator Anti Windup. In 2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC). pp. 101-104. https://doi.org/10.1109/ICCEREC.2016.7814981.

Budiastra, I. N., & Pemayun, A. M., (2020). Prototype Design of Water Level Control System Based on PID Controller in PLTMH. Journal of Electrical, Electronics and Informatics, Vol. 4, No. 2, pp. 53-56. https://doi.org/10.24843/jeei.2020.v04.i02.p03.

Chen, L., (2021). Principle and Simulation PID Controller of Liquid Level System. In Journal of Physics: Conference Series (Vol. 1757, No. 1, p. 012187). IOP Publishing. https://doi.org/10.1088/1742-6596/1757/1/012187.

Bhookya, J., Kumar, M. V., Kumar, J. R., & Rao, A. S., (2022). Implementation of PID controller for liquid level system using mGWO and integration of IoT application. Journal of Industrial Information Integration. Vol. 28. https://doi.org/10.1016/j.jii.2022.100368.

Hou, Z., Chi, R., & Gao, H., (2016). An overview of dynamic-linearization-based data-driven control and applications. IEEE Transactions on Industrial Electronics. Vol. 64, No.5, 4076-4090. https://doi.org/10.1109/tie.2016.2636126.

Yang, Y., Chen, C., & Lu, J., (2022). An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems. Entropy, 24(2), 163. https://doi.org/10.3390/e24020163.

Hou, Z., & Huang, W., (1997). The model-free learning adaptive control of a class of SISO nonlinear systems. In Proceedings of the 1997 American Control Conference (Cat. No. 97CH36041) (Vol. 1, pp. 343-344). https://doi.org/10.1109/acc.1997.611815.

Xie, R., Song, T., Shi, P., & Zhao, Y., (2016). Model-free Adaptive Control for Spacecraft Attitude. Journal of Harbin Institute of Technology (New Series). Vol. 23, No. 6.

Kadri, M. B., & Raazi, S. M., (2019). Model Free Fuzzy Adaptive Control For Networked Control Systems. Technology Forces Journal of Engineering and Sciences.Vol. 2, No. 1.

Mutambara, A. G., (2017). Design and analysis of control systems. CRC press.

Pratama, S. C., Susanto, E., & Wibowo, A. S., (2016). Design and implementation of water level control using gain scheduling PID back calculation integrator Anti Windup. In 2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC) (pp. 101-104). https://doi.org/10.1109/iccerec.2016.781498.

Getu, B. N., (2016). Water Level Controlling System Using Pid Controller. International Journal of Applied Engineering Research.Vol. 11, No. 23 pp. 11223-11227.

Aliaa A., Ekhlas H. K., (2022). Optimal Improved Pid Controller with Goa Algorithm For Single Link Human Leg Robot. Journal of Engineering and Sustainable Development (JEASD). ISSN: 2520-0917, Vol. 26, No. 02, pp. 103-110.

Ekhlas H. K. Noor S. A., Rokaia Sh. H, (2017). Design An Optimal Pid Controller With Nonlinear Function Using Bacteria Foraging Optimization For Single Flexible Link Robot Manipulator. Journal of Engineering and Sustainable Development (JEASD). ISSN: 2520-0917, Vol. 21, No.04, pp.14-29.

Swe, K. Y., & Dewan, L., (2015). Application of model free adaptive control in main steam temperature system of thermal power plant. International Journal of Electrical and Computer Engineering. Vol.9, No.3, 334-337. https://doi.org/10.5281/zenodo.1099856.

Yanling, Y., (2015). Model free adaptive control for robotic manipulator trajectory tracking. The Open Automation and Control Systems Journal. Vol. No. 7, pp. 358-365, https://doi.org/10.2174/1874444301507010358.

Sun, J., Lai, C. H., & Wu, X. J., (2016). Particle swarm optimisation: classical and quantum perspectives. Crc Press., ISBN: 13: 978-1-4398-3577-7.

Del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J. C., & Harley, R. G., (2008). Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on evolutionary computation. Vol. 12, No. 2, pp. 171–195. https://doi.org/10.1109/tevc.2007.896686.

Arain, B. A., Shaikh, M. F., Harijan, B. L., Memon, T. D., & Kalwar, I. H., (2018). Design of PID controller based on PSO algorithm and its FPGA synthesization. International Journal of Engineering and Advanced Technology. (IJEAT), ISSN: 2249 – 8958, Vol. 8, No. 2, PP. 201-206.

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Key Dates

Published

2023-05-01

How to Cite

Mohammed, Y. A., H. Karam, E. ., & N. Kadhim, N. . (2023). CONTROLLING WATER LEVEL BY USING MODIFIED MODEL FREE ADAPTIVE CONTROLLER . Journal of Engineering and Sustainable Development, 27(3), 375-383. https://doi.org/10.31272/jeasd.27.3.7

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