Abstract
A new approach for intelligent control is proposed for complex uncertain plants using synergism between multi-agent and ontology based frameworks. A multi stage procedure is developed for situation recognition, strategy selection and control algorithm parameterization following coordinated objective function. Fuzzy logic based extension of conventional ontology is implemented to meet uncertainties in the plant, its environment and sensor information. Ant colony optimization is applied to realize trade-off between requirements and control resources as well as for significant reduction of the communication rate among the intelligente agents. To react on unexpected changes in operational conditions certain adaptation functionality of the fuzzy ontology is foreseen. A multi-dimensional cascade system is considered and some simulation results are presented for variety of strategies implemented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Calegary, S., Sanchez, E.: A Fuzzy Ontology – Approach to Improve Semantic Information Retrieval. In: Proc. of 6th Int. Semantic Web Conference, Korea (2007)
Correas, L., Martinez, A., Volero, A.: Operation Diagnosis of a Combined Cycle based on Structural Theory of Thermoiconomics. In: ASME Int. Mechanical Engineering Congress and Exposition, Nashvill, USA (1999)
Dorigo, M., Birattari, M., Stützle, T.: Ant Colony Optimization. IEEE Computational Intelligence Magazine 1(4) (2006)
FIPA Specification (2006), http://www.fipa.org
Gonzalez, E.J., Hamilton, A., Moreno, L., Marichal, R.L., Toledo, J.: A MAS Implementation for System Identification and Process Control. Asian Journal of Control 8(4) (2006)
Haase, T., Weber, H., Gottelt, F., Nocke, J., Hassel, E.: Intelligent Control Solutions for Steam Power Plants to Balance the Fluctuation of Wind Energy. In: Proc. of the 17th World IFAC Congress, Seoul, Korea (2008)
Hadjiski, V.B.: Dynamic Ontology–based Approach for HVAC Control via Ant Colony Optimization. In: DECOM 2007, Izmir, Turkey (2007)
Hadjiski, M., Sgurev, V., Boishina, V.: Intelligent Agent-Based Non-Square Plants Control. In: Proc. of the 3-d IEEE Conference on Intelligent Systems, IS 2006, London (2006)
Hadjisk, M., Boishina, V.: Agent Based Control System for SITO Plant Using Stigmergy. In: Intern. Conf. Automatics and Informatics 2005, Sofia, Bulgaria (2005)
Herrera, S.I., Won, P.S., Reinaldo, S.J.: Multi-Agent Control System of a Kraft Recovery Boiler. In: Proc. of the 17th World IFAC Congress, Seoul, Korea (2008)
JADE 2007 (2007), http://jade.tilab.com
Lin, J.N.K.: Fuzzy Ontology-Based System for Product Management and Recommendation. International Journal of Computers 1(3) (2007)
Manesis, A., Sardis, D.J., King, R.E.: Intelligent Control of Wastewater Treatment Plants. Artifical Intelligence in Engineering 12(3) (1998)
Mitra, S., Gangadaran, M., Rajn, M., et al.: A Process Model for Uniform Transverse Temperature Distribution in a Sinter Plant. Steel Times International (4) (2005)
PiT Navigator, Advanced Combustion Control for Permanent Optimized ail/fuel Distribution, http://www.powitec.de
Valero, A., Correas, L., Lazzsreto, A., et al.: Thermoeconomic Philosophy Applied to the Operating Analysis and Diagnosis of Energy Systems. Int. J. of Thermodynamics 7(N2) (2004)
Ramos, V., Abraham, A.: ANTDIS: Self-organized Ant based Clustering Model for Intrustion Detection System, http://www.arxiv.org/pdf/cs/0412068.pdf
Volero, A., Correas, L., Serra, L.: Online Thermoeconomic Diagnosis of Thermal Power Plants. In: NATO ASI, Constantza, Rumania (1998)
Lee, C.-S.: Introduction to the Applications of Domain Ontology (2005), http://www.mail.nutn.edu.tw/~leecs/pdf/Leecs-SMC_Feature_Corner.pdf
Smirnov, D.N., Genkin, B.E.: Wastewater Treatment in Metal Processing, Metallurgy, Moskow (1989) (in Russian)
Stoilos, G., Stamon, G., Tzonvaras, V., Pan, J.Z., Horrocks, I.: Fuzzy OWL: Uncertainty and the Semantic Web. In: Proc. Int. Workshop OWL: Experience and Directions (2005)
Straccia, U.: Reasoning with Fuzzy Description Logics. Journal of Artificial Intelligence Research 14(2) (2001)
Oyarzabal, J.: Advanced Power Plant Scheduling. Economic and Emission Dispatch, Dispower (19) (2005)
Terpak, J., Dorcak, L., Kostial, I., Pivka, L.: Control of Burn – Through Point for Aglomeration Belt. Metallurgia 44(4) (2005)
Toeng, H.C.: Internet Application with Fuzzy Logic and Neural Network: A Survey. Journal of Engineering, Computing and Architecture 1(2) (2007)
Yang, Z., Ma, C., Feng, J.Q., Wu, O.H., Mann, S., Fitch, J.: A Multi – Agent Framework for Power System Automation. Int. Journal of Innovations in Energy System and Power 1(1) (2006)
Widyantorn, D.H., Yenn, J.: Using Fuzzy Ontology for Query Refinement in a Personalized Abstract Search Engine. In: Proc. of 9th IFSA World Congress, Vancouver, Canada (2001)
Wooldridge, M.: An Introduction to Multi–Agent Systems. John Wiley, Chichester (2002)
W3C, http://www.w3.org
Zadeh, L.: Fuzzy sets. Information and Control 8(3) (1965)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hadjiski, M., Sgurev, V., Boishina, V. (2010). Intelligent Control of Uncertain Complex Systems by Adaptation of Fuzzy Ontologies. In: Sgurev, V., Hadjiski, M., Kacprzyk, J. (eds) Intelligent Systems: From Theory to Practice. Studies in Computational Intelligence, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13428-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-13428-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13427-2
Online ISBN: 978-3-642-13428-9
eBook Packages: EngineeringEngineering (R0)