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Gantry and bridge cranes neuro-fuzzy control by using
neural-like structures of geometric transformations

Data publikacji: 16.09.2014

Czasopismo Techniczne, 2013, Automatyka Zeszyt 3-AC (11) 2013, s. 53 - 68

https://doi.org/10.4467/2353737XCT.14.057.3965

Autorzy

,
Iryna Verbenko
Lviv Polytechnic National University
Wszystkie publikacje autora →
Roman Tkachenko
Lviv Polytechnic National University
Wszystkie publikacje autora →

Tytuły

Gantry and bridge cranes neuro-fuzzy control by using
neural-like structures of geometric transformations

Abstrakt

Fuzzy logic is based on the use of natural language such as ‘far or close’, ‘cold or hot’ and etc. Its application range is very wide, from household appliances to the management of complex industrial processes. Many modern management tasks cannot be simply solved by classical methods because of the very great complexity of mathematical models. However, mathematical transformations are required for using the fuzzy logic theory on a computer and give a possibility to convert linguistic variables to their numerical value in the computer and vice versa. In this paper a gantry and bridge crane control system for managing carts swinging during transporting a load with high accuracy positioning during movement is presented. T-Controller fuzzy inference system as a base for crane management system is described and its main advantages in comparison with traditional systems are delineated. Schema of simplified crane model is introduced.

Bibliografia

Omar H.M., Control of Gantry and Tower Cranes, Dissertation submitted to the Faculty of the Virginia Polytechnic Institute, Virginia 2003, 100.

About T-Controller, Digital source, access by link: http://tkatchenko.com/t-controller/ about-t-controller/

Tkachenko O., Rule-based system of improved accuracy, Materials of 56th Interational scientific colloquium, Ilmenau: TU Ilmenau, 2011.

Tkachenko R., The new paradigm of the artificial neural networks straightforward dissemination, Visnyk of the Lviv Polytechnic National University, Computer Engineering and Information Technology, No. 386, 1999, 43-54.

Burul I., Kolonić F., Matuško J., The control system design of a gantry crane based on H∞ control theory, MIPRO, Croatia 2010, 183-188.

Popadic T., Kolonic F., Poljugan A., A fuzzy control scheme for the gantry crane position and load swing control, University of Zagreb, 6.

Tkachenko R., Tkachenko P., Tkachenko O., Schmitz J., Geometrikal data modelling, Intelligent systems of making decisions and applied aspects of information technology, Proceedings of the conference, Vol. 2, Eupatoria, 2006, 279-285.

Verbenko I., Tkachenko R., Fuzzy Methods and Tools for Crane Management System Based on T-Controller, Journal of Global Research in Computer Science, Vol. 4, No. 3, March 2013, 1-4.

Tkachenko R., Accelerated learning of multilayered neural networks on tha base of the new paradigma, Third Conference ‘Neural networks and their applications’, Kule 1997, 129-130.

Licata G., Fuzzy Logic, Knowledge and Natural Language, Fuzzy Inference System – Theory and Applications, Dr. Mohammad Fazle Azeem (Ed.), InTech, 2012, 504.

Dadios E.P., Fuzzy Logic – Controls, Concepts, Theories and Applications, InTech, 2012, 428.

Grigorie L., Fuzzy Controllers, Theory and Applications, InTech, 2011, 368.

Azar A.t., Fuzzy Systems, InTech, 2012, 216.

Yung C. Shin, Chengying Xu., Intelligent systems: modeling, optimization, and control, CRC Press Taylor & Francis Group, 2009, 456.

Leondes C.T., Fuzzy Logic and Expert Systems Applications, Academic Press, 1998, 417.

Kalogirou S., Artificial intelligence in energy and renewable energy systems, Nova Science Publishers, 2007, 473.

Koleshko V.M., Intelligent Systems, InTech, 2012, 366.

Kovacic Z., Bogdan S., Fuzzy controller design: theory and applications, CRC Press Taylor & Francis Group, 2006, 416.

Nguyen H.T., A first course in fuzzy and neural control, Chapman & Hall/CRC, 2003, 312.

Baaklini N., Mamdani E.H., Prescriptive methods for deriving control policy in a fuzzy-logic controller, Electron. Lett., Vol. 11, 1975, 625-626.

Bernard J.A., Use of rule-based system for process control, IEEE Contr. Syst. Mag., Vol. 8, No. 5, 1988, 3-13.

E.H. Mamdani, Applications of fuzzy algorithms for simple dynamic plant, Proc. IEE, Vol. 121, No. 12, 1974, 1585-1588.

Zadeh L.A., Outline of a new approach to the analysis complex systems and decision processes, IEEE Trans. Syst. Man Cybern., Vol. SMC-3, 1973, 28-44.

Bridging the Gap Between Conventional and Intelligent Control, IEEE Control Systems Magazine, Vol. 13, No. 3, 1993, 12-18.

Jantzen J., A Tutorial On Adaptive Fuzzy Control, EUNITE, Vol. 2002, 2012, 709-719.

Informacje

Informacje: Czasopismo Techniczne, 2013, Automatyka Zeszyt 3-AC (11) 2013, s. 53 - 68

Typ artykułu: Oryginalny artykuł naukowy

Tytuły:

Polski:

Gantry and bridge cranes neuro-fuzzy control by using
neural-like structures of geometric transformations

Angielski:

Gantry and bridge cranes neuro-fuzzy control by using
neural-like structures of geometric transformations

Autorzy

Lviv Polytechnic National University

Lviv Polytechnic National University

Publikacja: 16.09.2014

Status artykułu: Otwarte __T_UNLOCK

Licencja: Żadna

Udział procentowy autorów:

Iryna Verbenko (Autor) - 50%
Roman Tkachenko (Autor) - 50%

Korekty artykułu:

-

Języki publikacji:

Angielski