PERFORMANCE OF PSO BASED CLASSİCAL AND INTELLİGENT CONTROLLERS FOR WATER LEVEL CONTROL OF A STEAM GENERATOR
Yıl 2017,
Cilt: 19 Sayı: 57, 835 - 844, 01.09.2017
Emre Özkop
İsmail Hakkı Altaş
Öz
In this paper, different controller techniques based on particle swarm optimization (PSO) algorithm are proposed to control the water level of a steam generator with multiple inputmultiple output (MIMO) characteristics. The techniques employed are classical proportional+integral+derivative (PID) control, fuzzy logic control (FLC) and fuzzy tuned proportional-integral control (FTPIC). Gains of PID controller and parameters of FLC (the core and the boundaries of triangular membership functions in input and output spaces) are optimized by the PSO. Validations of the proposed PSO based PID control (PSO-PID), PSO based fuzzy logic control (PSO-FLC) and PSO based fuzzy tuned PI control (PSOFTPIC) techniques are done with numerical simulation in using MATLAB. The simulation results show that the PSO-PID provides better performance for controlling the water level of a steam generator compared to the others
Kaynakça
- [1] Liu, X.J., Lara-Rosano, F., Chan, C.W.
2003. Neurofuzzy Network
Modelling and Control of Steam
Pressure in 300 MW Steam-Boiler
System, Engineering Applications of
Artificial Intelligence, Vol. 16, No. 5-
6, pp. 431-440. DOI: 10.1016/ j.enga
ppai.2003.08.006
- [2] Klauco, M., Kvasnica, M. 2017.
Control of A Boiler-Turbine Unit
Using MPC-based Reference
Governors, Applied Thermal
Engineering, Vol. 110, pp. 1437-
1447. DOI: 10.1016/j.applthermale
ng.2016.09.041
- [3] Liu, X.J., Chan, C.W. 2006. NeuroFuzzy
Generalized Predictive
Control of Boiler Steam
Temperature, IEEE Transactions on
Energy Conversion, Vol. 21, No. 4,
pp. 900-908. DOI:
10.1109/TEC.2005. 853758
- [4] Ku, C.C. 2015. Robust Controller
Design for Nonlinear Uncertain
Stochastic Drum-Boiler System.
12th International Conference on
Fuzzy Systems and Knowledge
Discovery (FSKD), 15-17 August,
Zhangjiajie, China, 2378-2383.
- [5] Niva, L., Yli-Korpela, A. 2012.
Control of A Benchmark Boiler
Process Model with DMC and QDMC,
IFAC Proceedings, Vol. 45, No. 21,
pp. 190-195. DOI:
10.3182/20120902-4-FR2032.00035
- [6] Lawrynczuk, M. 2017. Nonlinear
Predictive Control of A BoilerTurbine
Unit: A State-Space
Approach with Successive On-Line
Model Linearisation and Quadratic
Optimisation, ISA Transactions, Vol.
67, pp. 476-495. DOI: 10.1016/
j.isatra.2017.01.016
- [7] Wei, L., Fang, F. 2017. H∞-LQRBased
Coordinated Control for
Large Coal-Fired Boiler-Turbine
Generation Units. IEEE
Transactions on Industrial
Electronics, Vol. 64, No. 6, pp. 5212-
5221. DOI: 10.1109/TIE.
2016.2622233
- [8] Moradi, H., Abbasi, M.H., Moradian,
H. 2016. Improving The
Performance of a Nonlinear BoilerTurbine
Unit Via Bifurcation Control
of External Disturbances: A
Comparison Between Sliding Mode
and Feedback Linearization Control
Approaches, Nonlinear Dynamics,
Vol. 85, No. 1, pp. 229-243. DOI:
10.1007/s11071-016-2680-x
- [9] Zhang F., Wu, X., Shen, J. 2017.
Extended State Observer Based
Fuzzy Model Predictive Control for
Ultra-Supercritical Boiler-Turbine
Unit, Applied Thermal Engineering,
Vol. 118, pp. 90-100. DOI: 10.1016/
j.applthermaleng.2017.01.111
- [10] Alamoodi, N., Daoutidis, P. 2017.
Nonlinear Control of Coal-Fired
Steam Power Plants, Control
Engineering Practice, Vol. 60, pp.
63-75. DOI:
0.1016/j.conengprac.2016. 12.005
- [11] González, J. A., Barreiro, A., Dormido,
S., Baños, A. 2017. Nonlinear
Adaptive Sliding Mode Control With
Fast Non-Overshooting Responses
And Chattering Avoidance, Journal
of the Franklin Institute, Vol. 354, No. 7, pp. 2788-2815. DOI: 10.1016/
j.jfranklin.2017.01.025
- [12] Ait Sahed, O., Kara, K., Benyoucef, A.,
Hadjili, M.L. 2016. An Efficient
Artificial Bee Colony Algorithm with
Application to Nonlinear Predictive
Control, International Journal of
General Systems, Vol. 45, No. 4, pp.
393-417. DOI: 10.1080/03081079.
2015.1086344
- [13] Jamali, B., Jazayeri-Rad, H. 2010.
Application of Adaptive Local Linear
Model Tree for Nonlinear
Identification of Heat Recovery
Steam Generator System Based on
Experimental Data. 2010 Fourth
UKSim European Symposium on
Computer Modeling and Simulation,
17-19 November, Pisa, Italy, 16-20.
- [14] Ozkop E., Sharaf A., Altas I.H. 2016.
An Adaptive Fuzzy PI Controlled
Bus Quantity Enhancer for Wave
Energy Systems, Turkish Journal of
Electrical Engineering and
Computer Sciences, Vol. 24, pp.
2454-2468. DOI: 10.3906/elk-1312-
198
- [15] Castro, P.A.D., Camargo, H.A. 2004.
Learning and Optimization of Fuzzy
Rule Base by Means of Self-Adaptive
Genetic Algorithm, IEEE
International Conference on Fuzzy
Systems; 25-29 July, Budapest,
Hungary. New York, NY, USA
- [16] Li, H.X., Gatland, H.B. 1995. New
Methodology for Designing A Fuzzy
Logic Controller, IEEE T Syst Man
Cy, Vol. 25, pp. 505-512
- [17] Altas, I.H., Sharaf, A.M. 2007. A
Generalized Direct Approach for
Designing Fuzzy Logic Controllers in
MATLAB/ Simulink GUI
Environment, International Journal
of Information Technology and
Intelligent Computing, Vol. 1, No. 4,
pp. 1-27
- [18] Liu ,J., Ma, D., Ma, T.B, Zhang, W.
2017. Ecosystem Particle Swarm
Optimization, Soft Computing, Vol.
21, No. 7, pp. 1667-1691. DOI:
10.1007/s00500-016-2111-4
- [19] Yang, Q.H., Tian, J.P., Si, W. 2017. An
Improved Particle Swarm
Optimization Based on Difference
Equation Analysis, Journal of
Difference Equations and
Applications, Vol. 23, No. 1-2, pp.
135-152. DOI: 10.1080/10236198.
2016.1199691
- [20] Cui, H.Q., Shu, M.L., Song, M., Wang,
Y.L. 2017. Parameter Selection and
Performance Comparison of Particle
Swarm Optimization in Sensor
Networks Localization, Sensors, Vol.
17, No. 3, pp. 1-18. DOI: 10.3390/
s17030487
- [21] Bouallègue, S., Haggège, J., Ayadi, M.,
Benrejeb, M. 2012. PID-Type Fuzzy
Logic Controller Tuning Based on
Particle Swarm Optimization,
Engineering Applications of
Artificial Intelligence, Vol. 25, No. 3,
pp. 484-493. DOI:
10.1016/j.engappai.2011. 09.018
- [22] Khare, A., Rangnekar, S. 2013. A
Review of Particle Swarm
Optimization and Its Applications in
Solar Photovoltaic System, Applied
Soft Computing, Vol. 13, No. 5, pp.
2997-3006. DOI: 10.1016/j.asoc.
2012.11.033
- [23] Coban, R. 2011. A Fuzzy Controller
Design for Nuclear Research
Reactors Using The Particle Swarm
Optimization Algorithm, Nuclear
Engineering and Design, Vol. 241,
No. 5, pp. 1899-1908. DOI: 10.1016/
j.nucengdes.2011.01.045
- [24] 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. DOI: 10.1109/
TEVC.2007.896686
BUHAR GENERATÖRÜNÜN SU SEVIYESI DENETIMI IÇIN PSO TEMELLI KLASIK VE AKILLI DENETLEYICILERIN PERFORMANSI
Yıl 2017,
Cilt: 19 Sayı: 57, 835 - 844, 01.09.2017
Emre Özkop
İsmail Hakkı Altaş
Öz
Bu çalışmada çok giriş-çok çıkış (ÇGÇÇ) özelliğine sahip buhar generatörünün su seviyesi denetimi için parçacık sürü optimizasyonu (PSO) algoritmasına dayanan farklı kontrol teknikleri önerilmektedir. Bu teknikler, klasik oransal-integraltürevsel (PID) denetim, bulanık mantık denetim (BMD) ve bulanık ayarlı oransal-integral denetimdir. PID denetleyicilerin kazançları ve BMD’nin parametreleri (giriş ve çıkıştaki üçgen üyelik fonksiyonların merkezleri ve sınırları) PSO tarafından en uygun hale getirilmektedir. Önerilen PSO temelli PID denetim (PSO-PID), PSO temelli bulanık mantık denetim (PSO-BMD) ve PSO temelli bulanık ayarlı PI denetim (PSO-BAPI) tekniklerinin gerçeklemesi, MATLAB kullanılarak sayısal benzetim ile doğrulanmaktadır. Benzetim sonuçları, buhar generatörünün su seviyesi denetim için PSO-PID tekniğini diğerlerine göre daha iyi performans sergilediğini göstermektedir
Kaynakça
- [1] Liu, X.J., Lara-Rosano, F., Chan, C.W.
2003. Neurofuzzy Network
Modelling and Control of Steam
Pressure in 300 MW Steam-Boiler
System, Engineering Applications of
Artificial Intelligence, Vol. 16, No. 5-
6, pp. 431-440. DOI: 10.1016/ j.enga
ppai.2003.08.006
- [2] Klauco, M., Kvasnica, M. 2017.
Control of A Boiler-Turbine Unit
Using MPC-based Reference
Governors, Applied Thermal
Engineering, Vol. 110, pp. 1437-
1447. DOI: 10.1016/j.applthermale
ng.2016.09.041
- [3] Liu, X.J., Chan, C.W. 2006. NeuroFuzzy
Generalized Predictive
Control of Boiler Steam
Temperature, IEEE Transactions on
Energy Conversion, Vol. 21, No. 4,
pp. 900-908. DOI:
10.1109/TEC.2005. 853758
- [4] Ku, C.C. 2015. Robust Controller
Design for Nonlinear Uncertain
Stochastic Drum-Boiler System.
12th International Conference on
Fuzzy Systems and Knowledge
Discovery (FSKD), 15-17 August,
Zhangjiajie, China, 2378-2383.
- [5] Niva, L., Yli-Korpela, A. 2012.
Control of A Benchmark Boiler
Process Model with DMC and QDMC,
IFAC Proceedings, Vol. 45, No. 21,
pp. 190-195. DOI:
10.3182/20120902-4-FR2032.00035
- [6] Lawrynczuk, M. 2017. Nonlinear
Predictive Control of A BoilerTurbine
Unit: A State-Space
Approach with Successive On-Line
Model Linearisation and Quadratic
Optimisation, ISA Transactions, Vol.
67, pp. 476-495. DOI: 10.1016/
j.isatra.2017.01.016
- [7] Wei, L., Fang, F. 2017. H∞-LQRBased
Coordinated Control for
Large Coal-Fired Boiler-Turbine
Generation Units. IEEE
Transactions on Industrial
Electronics, Vol. 64, No. 6, pp. 5212-
5221. DOI: 10.1109/TIE.
2016.2622233
- [8] Moradi, H., Abbasi, M.H., Moradian,
H. 2016. Improving The
Performance of a Nonlinear BoilerTurbine
Unit Via Bifurcation Control
of External Disturbances: A
Comparison Between Sliding Mode
and Feedback Linearization Control
Approaches, Nonlinear Dynamics,
Vol. 85, No. 1, pp. 229-243. DOI:
10.1007/s11071-016-2680-x
- [9] Zhang F., Wu, X., Shen, J. 2017.
Extended State Observer Based
Fuzzy Model Predictive Control for
Ultra-Supercritical Boiler-Turbine
Unit, Applied Thermal Engineering,
Vol. 118, pp. 90-100. DOI: 10.1016/
j.applthermaleng.2017.01.111
- [10] Alamoodi, N., Daoutidis, P. 2017.
Nonlinear Control of Coal-Fired
Steam Power Plants, Control
Engineering Practice, Vol. 60, pp.
63-75. DOI:
0.1016/j.conengprac.2016. 12.005
- [11] González, J. A., Barreiro, A., Dormido,
S., Baños, A. 2017. Nonlinear
Adaptive Sliding Mode Control With
Fast Non-Overshooting Responses
And Chattering Avoidance, Journal
of the Franklin Institute, Vol. 354, No. 7, pp. 2788-2815. DOI: 10.1016/
j.jfranklin.2017.01.025
- [12] Ait Sahed, O., Kara, K., Benyoucef, A.,
Hadjili, M.L. 2016. An Efficient
Artificial Bee Colony Algorithm with
Application to Nonlinear Predictive
Control, International Journal of
General Systems, Vol. 45, No. 4, pp.
393-417. DOI: 10.1080/03081079.
2015.1086344
- [13] Jamali, B., Jazayeri-Rad, H. 2010.
Application of Adaptive Local Linear
Model Tree for Nonlinear
Identification of Heat Recovery
Steam Generator System Based on
Experimental Data. 2010 Fourth
UKSim European Symposium on
Computer Modeling and Simulation,
17-19 November, Pisa, Italy, 16-20.
- [14] Ozkop E., Sharaf A., Altas I.H. 2016.
An Adaptive Fuzzy PI Controlled
Bus Quantity Enhancer for Wave
Energy Systems, Turkish Journal of
Electrical Engineering and
Computer Sciences, Vol. 24, pp.
2454-2468. DOI: 10.3906/elk-1312-
198
- [15] Castro, P.A.D., Camargo, H.A. 2004.
Learning and Optimization of Fuzzy
Rule Base by Means of Self-Adaptive
Genetic Algorithm, IEEE
International Conference on Fuzzy
Systems; 25-29 July, Budapest,
Hungary. New York, NY, USA
- [16] Li, H.X., Gatland, H.B. 1995. New
Methodology for Designing A Fuzzy
Logic Controller, IEEE T Syst Man
Cy, Vol. 25, pp. 505-512
- [17] Altas, I.H., Sharaf, A.M. 2007. A
Generalized Direct Approach for
Designing Fuzzy Logic Controllers in
MATLAB/ Simulink GUI
Environment, International Journal
of Information Technology and
Intelligent Computing, Vol. 1, No. 4,
pp. 1-27
- [18] Liu ,J., Ma, D., Ma, T.B, Zhang, W.
2017. Ecosystem Particle Swarm
Optimization, Soft Computing, Vol.
21, No. 7, pp. 1667-1691. DOI:
10.1007/s00500-016-2111-4
- [19] Yang, Q.H., Tian, J.P., Si, W. 2017. An
Improved Particle Swarm
Optimization Based on Difference
Equation Analysis, Journal of
Difference Equations and
Applications, Vol. 23, No. 1-2, pp.
135-152. DOI: 10.1080/10236198.
2016.1199691
- [20] Cui, H.Q., Shu, M.L., Song, M., Wang,
Y.L. 2017. Parameter Selection and
Performance Comparison of Particle
Swarm Optimization in Sensor
Networks Localization, Sensors, Vol.
17, No. 3, pp. 1-18. DOI: 10.3390/
s17030487
- [21] Bouallègue, S., Haggège, J., Ayadi, M.,
Benrejeb, M. 2012. PID-Type Fuzzy
Logic Controller Tuning Based on
Particle Swarm Optimization,
Engineering Applications of
Artificial Intelligence, Vol. 25, No. 3,
pp. 484-493. DOI:
10.1016/j.engappai.2011. 09.018
- [22] Khare, A., Rangnekar, S. 2013. A
Review of Particle Swarm
Optimization and Its Applications in
Solar Photovoltaic System, Applied
Soft Computing, Vol. 13, No. 5, pp.
2997-3006. DOI: 10.1016/j.asoc.
2012.11.033
- [23] Coban, R. 2011. A Fuzzy Controller
Design for Nuclear Research
Reactors Using The Particle Swarm
Optimization Algorithm, Nuclear
Engineering and Design, Vol. 241,
No. 5, pp. 1899-1908. DOI: 10.1016/
j.nucengdes.2011.01.045
- [24] 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. DOI: 10.1109/
TEVC.2007.896686