PID Control Performance Improvement for a Liquid Level System using Parameter Design

Abstract

In this study, it is aimed to reduce the variability of parameters in the liquid level system controlled by PID controller for a laboratory scale device. An integrated methodology consisting of experimental design and feedback PID (proportional-integral-derivative) controller was proposed to optimize and control the deviation from the average value in the offset value, variability in the offset value and the time to reach the set value in this liquid level system. The optimal valve opening levels that minimizes the average of the offset value (µ), variance (s²) and the first time to reach the set value (t) were determined as 40%, 5%, 50% and 80%, respectively, using TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution)-based Taguchi method by Minitab®. A quite successful control was established in the verification test which performed with specified levels of optimal valve opening. Recovery rates in the control performance before and after optimizing the parameter were calculated as 9.53% in the deviation from the average value in the offset values, 29.37% in the variability in the offset value and 11.27% in the time to reach the set value. MATLAB/Simulink was used to simulate the liquid level system.

Keywords

• Laboratory Scale Liquid Level System,TOPSIS based Taguchi Parameter Design

References

1. [1] M. Lee, J. Shin, “Constrained optimal control of liquid level loop using a conventional proportional-integral controller”, Taylor and Francis, Chem. Eng. Commun. vol. 196, pp. 729–745, 2009.
2. [2] Q. Wang, L. Xing, X. Shi, “Decoupling control of three tank liquid level systems based on feed forward compensation”, Proceedings of Chinese Control and Decision Conference (CCDC), pp. 5863– 5866, 2009.
3. [3] R. Paul, A. Sengupta, R.R. Pathak, “Wavelet based denoising technique for liquid level system”, Measurement, vol. 46, pp. 1979–1994, 2013.
4. [4] R. Zhang, A. Xue, S. Wang, “Modeling and nonlinear predictive functional control of liquid level in a coke fractionation tower”, Chemical Engineering Science, vol. 66 pp. 6002–6013, 2011.
5. [5] G. Peng, J. He, S. Yang, W. Zhou, “Application of the fiber-optic distributed temperature sensing for monitoring the liquid level of producing oil wells”, Measurement, vol. 58, pp. 130–137, 2014.
6. [6] Z. Aydoğmuş, “Implementation of a fuzzy-based level control using SCADA”, Expert Systems with Applications, vol. 36, pp. 6593–6597, 2009.
7. [7] D. Sbarbaro, R. Ortega, “Averaging level control of multiple tanks: A passivity based approach”, Journal of Process Control. Elsevier, 2007.
8. [8] N.B. Almutairi, M. Zribi, J. Hu, K. Bai, “Sliding mode control of coupled tanks”, Mechatronics, Elsevier, 2007.

9. [9] R. Zhang, S. Wu, F. Gao, “Improved PI controller based on predictive functional control for liquid level regulation in a coke fractionation tower”, J. Process Control, vol. 24, pp. 125-132, 2014.
10. [10] M.M. Noel, B.J. Pandian, “Control of a nonlinear liquid level system using a new artificial neuralnetwork based reinforcement learning approach” Applied Soft Computing, vol. 23, pp. 444–451, 2014.
11. [11] M.S. Sadeghi, B. Safarinejadian, A. Farughian, “Parallel distributed compensator design of tank level control based on fuzzy Takagi–Sugeno model”, Applied Soft Computing, vol. 21, pp. 280–285, 2014.
12. [12] A.P. Singh, S. Mukherjee, M. Nikolaou, “Debottlenecking level control for tanks in series”, Journal of Process Control, vol. 24, pp. 158–171, 2014.
13. [13] J. Tao, Z. Yu, Y. Zhu, “PFC Based PID Design using Genetic Algorithm for Chamber Pressure in a Coke Furnace”, Chemometrics and Intelligent Laboratory Systems, vol. 137 pp. 155–161, 2014.
14. [14] R. Zhang, S. Wu, R. Lu, F. Gao, “Predictive Control Optimization based PID Control for Temperature in an Industrial Surfactant Reactor”, Chemometrics and Intelligent Laboratory Systems, vol. 135, pp. 48–62, 2014.
15. [15] J. Zhang, S. Yang, “An incremental-PID-controlled Particle Swarm Optimization Algorithm for EEG-data-based Estimation of Operator Functional State”, Biomedical Signal Processing and Control, vol. 14, pp. 272–284, 2014.
16. [16] B.K. Sahu, S. Pati, P.K. Mohantya, S. Panda, “Teaching–Learning based Optimization Algorithm based fuzzy-PID Controller for Automatic Generation Control of Multi-Area Power System”, Applied Soft Computing, vol. 27 pp. 240–249, 2015.
17. [17] A.J.H. Al Gizi, M.W. Mustafa, K.M.A. Al Zaidi, M.K.J. Al-Zaidi, “Integrated PLC-fuzzy PID Simulink implemented AVR system”, Electrical Power and Energy Systems, vol. 69 pp. 313–326, 2015.
18. [18] B. Şimşek, Y.T. İç, H.E. Şimşek, “A TOPSIS based Taguchi Optimization to Determine Optimal Mixture Proportions of the High Strength Self-Compacting Concrete” Chemometrics and Intelligent Laboratuary Systems, vol. 125, pp. 18-32, 2013.
19. [19] D.E. Seborg, T.F. Edgar, D.A. Mellichamp, F.J. Doyle, “Process Dynamics and Control”, 3. rd., John-Wiley and Sons
20. [20] B. Şimşek, Y.T. İç, H.E. Şimşek, “A Full Factorial Design Based Desirability Function Approach for Optimization of Properties of C40/C50 Concrete Class”, Math. Comput. Appl., vol. 18, no 3, pp. 330–339, 2013.
21. [21] S.M. Phadke, “Quality Engineering Using Robust Design”, Prentice Hall, New Jersey, 1989.
22. [22] Y. Kuo, T. Yang, G.W. Huang, “The Use of a Grey-Based Taguchi Method for Optimizing Multi-Response Simulation Problems”, Engineering Optimization, vol. 40, no. 6, pp. 517-528, 2008.
23. [23] C.Y. Chang, R. Huang, P.C. Lee, T.L. Weng,. “Application of a Weighted Grey-Taguchi Method for Optimizing Recycled Aggregate Concrete Mixtures”, Cement Concrete Comp., vol. 33, pp.1038–1049,2011