A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process

ERGÜZEL, TÜRKER TEKİN

A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process

Türker Tekin ERGÜZEL (Department of Computer Engineering, Uskudar University, Turkey Email:)

An International Journal of Optimization and Control: Theories & Applications (IJOCTA)

0 1

Yıl: 2015 Cilt: 5 Sayı: 2 Sayfa Aralığı: 63 - 73 Metin Dili: İngilizce İndeks Tarihi: 29-07-2022

A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process

Öz:

Water level control is a crucial step for steam generators (SG) which are widely used to control the temperature of nuclear power plants. The control process is therefore a challenging task to improve the performance of water level control system. The performance assessment is another consideration to underline. In this paper, in order to get better control of water level, the nonlinear process was first expressed in terms of a transfer function (TF), a proportional-integral-derivative (PID) controller was then attached to the model. The parameters of the PID controller was finally optimized using particle swarm optimization (PSO). Simulation results indicate that the proposed approach can make an effective tracking of a given level set or reference trajectory.

Anahtar Kelime:

Konular: Matematik İstatistik ve Olasılık

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

[1] Zhang, Z., Performance assessment for the water level control system in steam generator of the nuclear power plant, Annals of Nuclear Energy 45, 94-105 (2012).
[2] Irving, E., Miossec, C., Tassart, J., Towards efficient full automatic operation of the PWR steam generator with water level adaptive control. In: Proceedings of the 2nd International Conference on Boiler Dynamics and Control in Nuclear Power Stations, London, 309-29 (1980).
[3] Na, M.G., No, H.C., Design of an adaptive observer-based controller for the water level of steam generators. Nuclear Engineering and Design, 135(3), 379-94 (1992).
[4] Parlos, A.G., Rais O.T., Nonlinear control of U-tube steam generators via H? control. Control Engineering Practice,8,921-36 (2000).
[5] Kothare, M.V., Mettler, B., Morari, M., Level control in the steam generator of a nuclear power plant. IEEE Control Systems Technology 8(1), 55-69 (2000).
[6] Na, M.G., Sim, Y.R., Joon, L.Y., Design of an adaptive predictive controller for steam generators. IEEE Transactions Nuclear Science 50(1), 186-93 (2003).
[7] Cho, B.H., No, H.C., Design of a geneticfuzzy controller for the nuclear steam generator water level control. IEEE Transactions Nuclear Science 45(4), 2261-71 (1998).
[8] Munasinghe, S.R., Kim, M.S., Lee, J.J., Adaptive neurofuzzy controller to regulate UTSG water level in nuclear power plants. IEEE Transactions Nuclear Science 52(1), 421-9 (2005).
[9] Hu, K., Yuan, J., Multi-model predictive control method for nuclear steam generator water level, Energy Conversion and Management 49, 1167-1174 (2008).
[10] Mohamed, I., Statistical analysis of neural network modeling and identification of nonlinear systems with memory,IEEE Transections on Signal Processing, 50(6) (2002).
[11] Zhang, Y., Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM, Chemical Engineering Science, 64, 801- 811 (2009).
[12] Jiang, C., Deng, M., Inoue, A., Operator based Nonlinear Control Design for a Water Level Process System, Proceedings of the 17th World Congress The International Federation of Automatic Control Seoul, Korea, July 6-11 (2008).
[13] Ch, S., Anand, N., Panigrahi, B., Streamflow forecasting by SVM with quantum behaved particle swarm optimization, Neurocomputing, 101, 18-23 (2013).
[14] Prexian, L, Zhixiang T.,Lili Y., A method to calculate displacement factors using SVM, Mining Science and Technology (China) 21, 307-311 (2011).
[15] Gunawardena, Y., Ilic, S., Pinkerton, H., Nonlinear transfer function modelling of beach morphology at Duck, North Carolina, Coastal Engineering, 56, 46-58 (2009).
[16] Towill, D.R., Transfer function techniques for control engineers. Iliffe Books, London, (1970).
[17] Mikles, J., Fikar, M., Process modelling, identification, and control. Springer, Berlin; NY, (2007).
[18] Sayyafzadeh, M., Pourafshary, P., Application of transfer functions to model water injection in hydrocarbon reservoir, Journal of Petroleum Science and Engineering, 78, 139-148 (2011).
[19] Muhammad, M.I., Wang, L., Fei, M., Pan, H., Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design, Expert Systems with Applications, 39, 4390-4401 (2012).
[20] Bingul, Z., A new PID tuning technique using differential evolution for unstable and integrating processes with time delay. InICONIP, proceedings lecture notes in computer science. 3316, 254-260 (2004).
[21] Chang, W.D. A multi-crossover genetic approach to multivariable PID controllers tuning.Expert Systems with Applications, 33, 620-626 (2007).
[22] Chang, W.D. PID control for chaotic synchronization using particle swarm optimization.Chaos, Solitons and Fractals, 39(2), 910-917 (2009).
[23] Chen, B.S., Cheng, Y.M., A genetic approach to mixed H2/H1 optimal PID control.IEEE Control Systems, 15(5), 51-60 (1995).
[24] Coelho, L. S., Bernert, D.L., PID control design for chaotic synchronization using tribes optimization approach. Chaos, Solitons and Fractals, 42(1), 634-640 (2009).
[25] Duan, H., Wang, D., Yu, X., Novel approach to nonlinear PID parameter optimization using ant colony optimization algorithm. Journal of Bionic Engineering, 3(2), 73-78 (2006).
[26] Kim, T. H., Maruta, I., & Sugie, M.. Robust PID controller tuning based on the constrained particle swarm optimization.Automatica, 44(4), 1104-1110 (2008).
[27] Mukherjee, V., Goshal, S.P., Particle swarm optimized fuzzy PID controller for AVR system.Electric Power System Research, 77(12), 1689-1698 (2007).
[28] Zhang, J., Zhuang, J., Du, H., & Wang, S. Self-organizing genetic algorithm based tuning of PID controllers.Information Sciences, 179(7), 1007-1018 (2009).
[29] Rudy, J., Dominik, A., Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features, An International Journal of Optimization and Control: Theories & Application (IJOCTA), 5 (1), 35-43 (2015).
[30] Tran, V.T., Kazushi, S., Genetic algorithm for optimization in adaptive bus signal priority control, An International Journal of Optimization and Control: Theories & Application (IJOCTA), 3 (1), 1-11 (2013).
[31] Salem, M., Khelfi M., Optimization of nonlinear controller with an enhanced biogeography approach An International Journal of Optimization and Control: Theories & Application (IJOCTA), 4(2), 77-87 (2014).
[32] Bassi, S.J., Mishra, M.K., Omizegba, E.E., Automatic tuning of proportional-integral- derivative controller using particle swarm optimization algorithm, IJAIA, 2, 4 (2011).
[33] Zhao, S.Z., Suganthan, P.N., Diversity enhanced particle swarm optimizer for global optimization of multimodal problems. IEEE Congress on Evolutionary Computation, 590- 597 (2009).
[34] Baskar, S., Suganthan, P.N., A novel concurrent particle swarm optimization. InCongress on evolutionary computation, CEC2004, 1,792-796 (2004).
[35] Van den Bergh, F., Engelbrecht, A.P., A Cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 225-239 (2004).
[36] Ahmed, A., Imtiaz, S., James, L., Designing Laboratory Procedures to Enhance Graduate Attributes, Proc. of Canadian Engineering Education Association (CEEA13) Conference (2013).
[37] Topuz, V., Fuzzy-Genetic Process Control, Thesis (PhD). Marmara University (2002).
[38] Erguzel, T.T., Fuzzy controller parameter tuning using ant colony optimization and genetic algorithms, Thesis (PhD). Marmara University (2010).
[39] Lieslehto J., PID controller tuning using Evolutionary programming, American Control Conference, V.A., 25-27 (2001).
[40] Osman, M., Infis, A., Abied, W., Elfandi, S., Tuning PID Controller Based On the SWARM Intelligence, Int.ernational Conf. on Innovations in Engineering and Technology Bangkok (2013).
[41] Zeng, G.Q., Chen. J., Chen, M.R., Design of multivariable PID controllers using real-coded population-based extremal optimization, Neurocomputing 151, 1343-1353 (2015).
[42] Soomro, W.A., Elamvazuthi, I., Khan, A., PID Controller Optimization using Artificial Fish Swarm Algorithm, International Journal of Electrical and Electronics Research (IJEER) 1,11-18 (2013).
[43] Ding, J., Liu, J., Chowdhury, K.R., Zhang, W., Hu, Q., A Particle Swarm Optimization using Local Stochastic Search and Enhancing Diversity for Continuous Optimization, Neurocomputing 137, 261-267 (2014).
[44] Vargas, D.E., Gutierrz, D., Arevalo I.L., Performance of different metaheuristics in EEG source localization compared to the Cramér-Rao bound, Neurocomputing 120, 597-609 (2013).
[45] Chen, F., Tang, B., Song, T., Li, L., Multifault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization, Measurement 47, 576-590 (2014).
[46] Clerc, M., L'optimisation par Essaim Particulaire: Versions Parame ´triques et Adaptatives, Hermes Science Publications, Lavoisier, Paris (2005).
[47] Killani, R., Rao, K.S., Satapathy, S., Effective document clustering with particle swarm optimization, in: B.K. Panigrahi et al. (Eds.): LNCS 6466, Proceedings of SEMCCO 2010, Springer-Verlag, Berlin/Heidelberg: 623-629 (2010).
[48] Liu, H., Abraham, A., Zhang, W., A fuzzy adaptive turbulent particle swarm optimization, International Journal of Innovative Computing and Applications Archive 1,1 (2007).
[49] Nouaouria, N., Boukadoum, M., Proulx, R., Particle swarm classification: A survey and positioning, Pattern Recognition 46, 2028- 2044 (2013).
[50] Garcia, N.P., Gonzalo, E.G., Fernandez, A., Muniz, C.D., Hybrid PSO-SVM-based method for long-term forecasting of turbidity in the Nalón river basin: A case study in Northern Spain, Ecological Engineering, 73, 192-200 (2014). Asst. Prof. Dr. Turker Tekin Erguzel received his Ph.D. degree in real time system modeling, controlling and parameter optimization from Marmara University. His research interests focus on real time systems, artificial intelligence and fuzzy control. He has been teaching at Uskudar University in the Computer Engineering Department of Faculty of Engineering and Natural Sciences since 2012.

APA	ERGÜZEL T (2015). A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. , 63 - 73.
Chicago	ERGÜZEL TÜRKER TEKİN A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. (2015): 63 - 73.
MLA	ERGÜZEL TÜRKER TEKİN A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. , 2015, ss.63 - 73.
AMA	ERGÜZEL T A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. . 2015; 63 - 73.
Vancouver	ERGÜZEL T A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. . 2015; 63 - 73.
IEEE	ERGÜZEL T "A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process." , ss.63 - 73, 2015.
ISNAD	ERGÜZEL, TÜRKER TEKİN. "A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process". (2015), 63-73.

APA	ERGÜZEL T (2015). A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 5(2), 63 - 73.
Chicago	ERGÜZEL TÜRKER TEKİN A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 5, no.2 (2015): 63 - 73.
MLA	ERGÜZEL TÜRKER TEKİN A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), vol.5, no.2, 2015, ss.63 - 73.
AMA	ERGÜZEL T A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. An International Journal of Optimization and Control: Theories & Applications (IJOCTA). 2015; 5(2): 63 - 73.
Vancouver	ERGÜZEL T A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process. An International Journal of Optimization and Control: Theories & Applications (IJOCTA). 2015; 5(2): 63 - 73.
IEEE	ERGÜZEL T "A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process." An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 5, ss.63 - 73, 2015.
ISNAD	ERGÜZEL, TÜRKER TEKİN. "A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process". An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 5/2 (2015), 63-73.