Abstract
This paper presents the developed by the authors generalized step-by-step method of synthesis and optimization of green fuzzy controllers (FC) for the automatic control systems (ACS) of the reactor’s temperature of the specialized pyrolysis plants (SPP). The proposed method gives the opportunity to synthesize and optimize Mamdani type green FCs of the temperature modes of the SPPs reactors that provide (a) high accuracy and quality indicators of temperature control, (b) low energy consumption in the process of functioning as well as (c) relatively simple software and hardware implementation. The initial synthesis of the structure and parameters of green FCs is implemented on the basis of expert assessments and recommendations. Their further optimization for improving the quality indicators, reducing energy consumption and simplification of soft/hardware realization is carried out using specific optimization procedures by means of mathematical programming methods. In order to study and validate the effectiveness of the developed method the design of the Mamdani type green FC for the temperature ACS of the pyrolysis reactor of the experimental SPP has been carried out in this work. The developed green FC has a relatively simple hardware and software implementation as well as allows to achieve high quality indicators of temperature modes control at a sufficiently low energy consumption, that confirms the high efficiency of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ryzhkov, S.S., Markina, L.M.: Experimental researches of organic waste recycling method of multiloop circulating pyrolysis. J. Collected Works NUS 5, 100–106 (2007). (in Russian)
Markina, L.M.: Development of new energy-saving and environmental safety technology at the organic waste disposal by ecopyrogenesis. J. Collected Works NUS 4, 8 (2011). (in Ukrainian)
Kondratenko, Y.P., Korobko, O.V., Kozlov, O.V.: PLC-based systems for data acquisition and supervisory control of environment-friendly energy-saving technologies. In: Kharchenko, V., Kondratenko, Y., Kacprzyk J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, vol. 74, pp. 247–267. Springer International Publishing, Berlin, Heidelberg (2017). https://doi.org/10.1007/978-3-319-44162-7_13
Kondratenko, Y.P., Kozlov, O.V.: Mathematic modeling of reactor’s temperature mode of multiloop pyrolysis plant. In: Modeling and Simulation in Engineering, Economics and Management. Lecture Notes in Business Information Processing, vol. 115, pp. 178–187 (2012). https://doi.org/10.1007/978-3-642-30433-0_18
Kondratenko, Y.P., Kozlov, O.V., Kondratenko, G.V., Atamanyuk, I.P.: Mathematical model and parametrical identification of ecopyrogenesis plant based on soft computing techniques. In: Berger-Vachon, C., Lafuente, A.M.G., Kacprzyk, J., Kondratenko, Y., Merigó, J.M., Morabito, C.F. (eds.) Complex Systems: Solutions and Challenges in Economics, Management and Engineering. Studies in Systems, Decision and Control, vol. 125, pp. 201–233. Springer International Publishing, Berlin, Heidelberg (2018). https://doi.org/10.1007/978-3-319-69989-9_13
Kondratenko, Y.P., Kozlov, O.V.: Mathematical model of ecopyrogenesis reactor with fuzzy parametrical identification. In: Zadeh, L.A., et al. (eds.) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol. 342, pp. 439–451. Springer-Verlag, Berlin, Heidelberg (2016). https://doi.org/10.1007/978-3-319-32229-2_30
Kharchenko, V., et al. (eds.).: Green IT engineering: concepts, models, complex systems architectures. In: Decision and Control, vol. 74. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44162-7
Kharchenko, V., et al. (eds.).: Green IT engineering: components, networks and systems implementation. In: Studies in Systems, Decision and Control, vol. 105. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55595-9
Drozd, J., Drozd, A., Antoshchuk, S.: Green IT engineering in the view of resource-based approach. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, vol. 74, pp. 43–65. Springer International Publishing, Berlin, Heidelberg (2017). https://doi.org/10.1007/978-3-319-44162-7_3
Palagin, A.V., Opanasenko, V.N.: Design and application of the PLD-based reconfigurable devices. In: Adamski, M., Barkalov, A., Wegrzyn, M. (eds.) Design of Digital Systems and Devices. Lecture Notes in Electrical Engineering, vol. 79, pp. 59–91. Verlag, Springer, Berlin, Heidelberg (2011)
Kharchenko, V., Illiashenko, O.: Concepts of green IT engineering: taxonomy, principles and implementation. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol. 74, pp. 3–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44162-7_1
Kuchuk, G., Kovalenko, A., Kharchenko, V., Shamraev, A.: Resource-oriented approaches to implementation of traffic control technologies in safety-critical I&C systems. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Components, Networks and Systems Implementation, Studies in Systems, Decision and Control, vol. 105, pp. 313–337. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55595-9_15
Kondratenko, Y., Gordienko, E.: Implementation of the neural networks for adaptive control system on FPGA. In: Katalinic, B. (ed.) Annals of DAAAM for 2012, Proceeding of the 23th International DAAAM Symposium on “Intelligent Manufacturing and Automation”, vol. 23, no. 1, pp. 0389–0392. DAAAM International, Vienna, Austria, EU (2012)
Zadeh, L.A., Abbasov, A.M., Yager, R.R., Shahbazova, S.N., Reformat, M.Z. (eds.): Recent Developments and New Directions in Soft Computing. In: STUDFUZ, vol. 317. Springer, Cham (2014)
Jamshidi, M., Kreinovich, V., Kacprzyk, J. (eds.): Advance Trends in Soft Computing. Springer-Verlag, Cham (2013)
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall (1996)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Zadeh, L.A.: The role of fuzzy logic in modeling, identification and control. Model. Ident. Control 15(3), 191–203 (1994)
Piegat, A.: Fuzzy Modeling and Control, vol. 69. Physica (2013)
He, X., He, S.: Research of energy consumption pattern classification based on fuzzy logic and RBF networks in hydraulic systems. Sens. Transducers 205(10), 58–62 (2016)
Xiao, Z., Guo, J., Zeng, H., Zhou, P., Wang, S.: Application of fuzzy neural network controller in hydropower generator unit. J. Kybernetes 38(10), 1709–1717 (2009)
Kondratenko, Y., Gerasin, O., Topalov, A.: A simulation model for robot’s slip displacement sensors. Int. J. Comput. 15(4), 224–236 (2016). Retrieved from http://computingonline.net/computing/article/view/854
Pasieka, M., Grzesik, N., Kuźma, K.: Simulation modeling of fuzzy logic controller for aircraft engines. Int. J. Comput. 16(1), 27–33 (2017). Retrieved from http://computingonline.net/computing/article/view/868
Gomolka, Z., Dudek-Dyduch, E., Kondratenko, Y.P.: From homogeneous network to neural nets with fractional derivative mechanism. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. Lecture Notes in Artificial Intelligence 10245, 16th International Conference ICAISC 2017, Zakopane, Poland, 11–15 June 2017. Proceedings, Part 1, pp. 52–63. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_5
Tanaka, K., Wang, H.O.: Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach. Wiley, New York, USA (2001)
Hampel, R., Wagenknecht, M., Chaker N.: Fuzzy Control: Theory and Practice, p. 410. Physika-Verlag, Heidelberg, New York (2000)
Kondratenko, Y.P., Kozlov, O.V., Kondratenko, G.V., Atamanyuk, I.P.: Mathematical model and parametrical identification of ecopyrogenesis plant based on soft computing techniques. In: Berger-Vachon, C. et al. (eds.) Complex Systems: Solutions and Challenges in Economics, Management and Engineering, Studies in Systems, Decision and Control, vol. 125, pp. 201–233. Springer, Berlin, Heidelberg (2018). https://doi.org/10.1007/978-3-319-69989-9_13
Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Control. Springer Science & Business Media (2013)
Merigo, J.M., Gil-Lafuente, A.M., Yager, R.R.: An overview of fuzzy research with bibliometric indicators. Appl. Soft Comput. 27, 420–433 (2015)
Rotshtein, A.P., Rakytyanska, H.B.: Fuzzy Evidence in Identification, Forecasting and Diagnosis, vol. 275. Springer, Heidelberg (2012)
Von Altrock, C.: Applying fuzzy logic to business and finance. Optimus 2, 38–39 (2002)
Suna, Q., Li, R., Zhang, P.: Stable and optimal adaptive fuzzy control of complex systems using fuzzy dynamic model. J. Fuzzy Sets Syst. 133, 1–17 (2003)
Oh, S.K., Pedrycz, W.: The design of hybrid fuzzy controllers based on genetic algorithms and estimation techniques. J. Kybernetes 31(6), 909–917 (2002)
Lodwick, W.A., Kacprzhyk, J. (eds.).: Fuzzy optimization. In: STUDFUZ, vol. 254. Springer-Verlag, Berlin, Heidelberg (2010)
Kondratenko, Y.P., Simon, D.: Structural and parametric optimization of fuzzy control and decision making systems. In: Zadeh, L. et al. (eds.) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol. 361, pp. 273–289 (2018). Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_22
Jayaram, B.: Rule reduction for efficient inferencing in similarity based reasoning. Int. J. Approximate Reasoning 48(1), 156–173 (2008)
Yam, Y., Baranyi, P., Yang, C.-T.: Reduction of fuzzy rule base via singular value decomposition. IEEE Trans. Fuzzy Syst. 7(2), 120–132 (1999)
Kondratenko, Y.P., Al Zubi, E.Y.M.: The optimization approach for increasing efficiency of digital fuzzy controllers. In: Annals of DAAAM for 2009, Proceeding of the 20th International DAAAM Symposium “Intelligent Manufacturing and Automation”, pp. 1589–1591. DAAAM International, Vienna, Austria (2009)
Simon, D.: H∞ estimation for fuzzy membership function optimization. Int. J. Approximate Reasoning 40, 224–242 (2005)
Simon, D.: Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence. Wiley (2013)
Kondratenko, Y.P., Klymenko, L.P., Al Zu’bi, E.Y.M.: Structural optimization of fuzzy systems’ rules base and aggregation models. Kybernetes 42(5), 831–843 (2013). http://dx.doi.org/10.1108/K-03-2013-0053
Simon, D.: Design and rule base reduction of a fuzzy filter for the estimation of motor currents. Int. J. Approximate Reasoning 25, 145–167 (2000)
Ishibuchi, H., Yamamoto, T.: Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst. 141(1), 59–88 (2004)
Koczy, L.T., Hirota, K.: Size reduction by interpolation in fuzzy rule bases. IEEE Trans. Syst. Man Cybern. B Cybern. 27(1), 14–25 (1997)
Alcalá, R., Alcalá-Fdez, J., Gacto, M.J., Herrera, F.: Rule base reduction and genetic tuning of fuzzy systems based on the linguistic 3-tuples representation. Soft. Comput. 11(5), 401–419 (2007)
Pedrycz, W., Li, K., Reformat, M.: Evolutionary reduction of fuzzy rule-based models. In: Fifty Years of Fuzzy Logic and Its Applications, STUDFUZ, vol. 326, pp. 459–481. Springer, Cham (2015)
Simon, D.: Sum normal optimization of fuzzy membership functions. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10, 363–384 (2002)
Simon, D.: Training fuzzy systems with the extended Kalman filter. Fuzzy Sets Syst. 132, 189–199 (2002)
Kondratenko, Y., Korobko, V., Korobko, O., Kondratenko, G., Kozlov, O.: Green-IT approach to design and optimization of thermoacoustic waste heat utilization plant based on soft computing. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Components, Networks and Systems Implementation. Studies in Systems, Decision and Control, vol. 105, pp. 287–311. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55595-9_14
Kondratenko, Y.P., Kozlov, O.V., Korobko, O.V., Topalov, A.M.: Synthesis and optimization of fuzzy control system for floating Dock’s docking operations. In: Santos, W. (ed.) Fuzzy Control Systems: Design, Analysis and Performance Evaluation, pp. 141–215. Nova Science Publishers, Hauppauge, NY (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kozlov, O., Kondratenko, G., Gomolka, Z., Kondratenko, Y. (2019). Synthesis and Optimization of Green Fuzzy Controllers for the Reactors of the Specialized Pyrolysis Plants. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds) Green IT Engineering: Social, Business and Industrial Applications. Studies in Systems, Decision and Control, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-030-00253-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-00253-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00252-7
Online ISBN: 978-3-030-00253-4
eBook Packages: EngineeringEngineering (R0)