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Erschienen in:
Determination of Operating Parameters and Performance Analysis of Computer Networks with Paraconsistent Annotated Evidential Logic Eτ Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Container Crane Controller with the Use of a NeuroFuzzy Network

verfasst von : Ricardo Pinto Ferreira, Andréa Martiniano, Arthur Ferreira, Marcio Romero, Renato Jose Sassi

Erschienen in: Advances in Production Management Systems. Initiatives for a Sustainable World

Verlag: Springer International Publishing

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Abstract

A container crane has the function of transporting containers from one point to another point. The difficulty of this task lies in the fact that the container is connected to the bridge crane by cables, causing an opening angle while the container is being transported, interfering with the operation at high speeds due to oscillation that occurs at the end point, which could cause accidents. Fuzzy logic (FL) is a mathematical theory that aims to allow the modeling of approximate way of thinking, imitating the human ability to make decisions in uncertain and imprecise environments. The Artificial Neural Networks (ANN) models are made of simple processing units, called artificial neurons, which calculate mathematical functions. The aim of the paper was to present a container crane controller pre-project using an artificial neural network type Multilayer Perceptron (MLP) combined with FL, referred to as Neuro Fuzzy Network (NFN).

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Vorheriges Kapitel Combining Genetic Algorithm with Constructive and Refinement Heuristics for Solving the Capacitated Vehicle Routing Problem
Nächstes Kapitel Agility Challenges in Finnish Manufacturing Companies – Manufacturing Operations Management Viewpoint
Literatur
1.
2.
Pacheco, M.A.C., Vellasco, M.M.B.R.: Sistemas Inteligentes de Apoio à Decisão: Análise Econômica de Projetos de Desenvolvimento de Campos de Petróleo Sob Incerteza. Interciência, Rio de Janeiro (2007)
3.
Costa, H.C.: Aplicação de Técnicas de Modelagem e Controle em Sistemas Tipo Ponte Rolante. Dissertação de Mestrado, Instituto Militar de Engenharia, Rio de Janeiro (2010)
4.
Haykin, S.: Redes Neurais – Princípios e Práticas. Bookman, Porto Alegre (2001)
5.
Bigus, J.P.: Data Mining with Neural Network: Solving Business Problems from Applications Development to Decision Support. McGraw-Hill, New York (1996)
6.
Maass, W., Bishop, C.M.: Pulsed Neural Networks. First MIT Press Paperback Edition, Cambridge (2001)MATH
7.
Silva, I.N., Spatti, D.H., Flauzino, R.A.: Redes Neurais Artificiais para Engenharia e Ciências Aplicadas. Artliber, São Paulo (2010)
8.
Braga, A.P., Carvalho, A.C.P.L.F., Ludermir, T.B.: Redes Neurais Artificiais: Teoria e Aplicações. LTC, Rio de Janeiro (2011)
9.
Goldschmidt, R., Passos, E., Bezerra, E.: Data Mining: Conceitos, Técnicas, Algoritmos, Orientações e Aplicações. Elsevier, Rio de Janeiro (2015)
10.
Ferreira, R.P., Martiniano, A., Ferreira, A., Ferreira, A., Sassi, R.J.: Study on daily demand forecasting orders using artificial neural network. IEEE Latin Am. Trans. 14(3), 1519–1525 (2016)CrossRef
11.
Sassi, R.J.: An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network. Pesqui. Oper. 32(1), 139–164 (2012)CrossRefMATH
12.
Mohammed, T., Hayajneh, S.M., Radaideh, I., Smadi, A.: Fuzzy logic controller for overhead cranes. Eng. Comput. 23(1), 84–98 (2006)CrossRefMATH
13.
Chang, C.Y.: Adaptive fuzzy controller of the overhead crane with nonlinear disturbances. IEEE Trans. Industr. Inf. 3(2), 164–172 (2007)CrossRef
14.
Yu, W., Moreno-Armendariz, M.A., Rodriguez, F.O.: Stable adaptive compensation with fuzzy CMAC for an overhead crane. Inf. Sci. 181, 4895–4907 (2011)MathSciNetCrossRefMATH
15.
Zhao, Y., Gao, H.J.: Fuzzy-model-based control of an overhead crane with input delay and actuator saturation. IEEE Trans. Fuzzy Syst. 20, 181–186 (2012)CrossRef
16.
Smoczek, J., Szpytko, J.: Evolutionary algorithm-based design of a fuzzy TBF predictive model and TSK fuzzy anti-sway crane control system. J. Eng. Appl. Artif. Intell. 28, 190–200 (2014)CrossRef
17.
Qian, D., Tong, S., Lee, S.: Fuzzy-logic-based control of payloads subjected to double-pendulum motion in overhead cranes. J. Autom. Constr. 65, 133–143 (2016)CrossRef
18.
Sadati, N., Hooshmand, A.: Design of again-scheduling anti-sway controller or tower cranes using fuzzy clustering techniques. In: Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automation, Sydney, Australia (2006)
19.
Lee, L.H., Huang, P.H., Shih, Y.C., Chiang, T., Chang, C.: Parallel neural network combined with sliding mode control in overhead crane control system. J. Vib. Control 120(5), 749–760 (2014)CrossRef
20.
Nguyen, Q.C., Ngo, H.Q.T., klin, W.H.: Nonlinear adaptive control of a 3D overhead crane. In: 15th International Conference on Control, Automation and Systems (ICCAS), Busan, Korea, pp. 41–47 (2015)
21.
Smoczek, J.: Fuzzy crane control with sensor less payload deflection feedback for vibration reduction. J. Mech. Syst. Signal Process. 46, 70–81 (2014)CrossRef
22.
Peng-Cheng, W., Yong-Chun, F., Zi-Ya, J.: A direct swing constraint-based trajectory planning method for underactuated overhead cranes. Acta Automatica Sinica 40(11), 2414–2419 (2014)CrossRef
23.
Passino, K.M., Yurkovich, S.: Fuzzy Control. Addison Wesley Longman, Inc., Boston (1998)MATH
24.
Nicoletti, M.C., Camargo, H.A.: Fundamentos da Teoria de Conjuntos Fuzzy. Edusfscar, São Carlos (2004)
25.
McNeill, F.M., Thro, E.: Fuzzy Logic: A Practical Approach. AP Professional/Academic Press, Boston (1994)MATH
26.
Simões, M.G., Shaw, I.S.: Controle E Modelagem Fuzzy. Blucher, São Paulo (2007)
27.
Rosenblatt, M.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386–408 (1958)CrossRef
28.
Mendel, J.M., Mclaren, R.W.: Reinforcement-learning control and pattern recognition systems. In: Adaptive, Learning and Pattern Recognition Systems, Cap. 8, pp. 287–318. Academic Press, New York e London (1970)
Metadaten
Titel
Container Crane Controller with the Use of a NeuroFuzzy Network
verfasst von
Ricardo Pinto Ferreira
Andréa Martiniano
Arthur Ferreira
Marcio Romero
Renato Jose Sassi
Copyright-Jahr
2016
Buch
Advances in Production Management Systems. Initiatives for a Sustainable World

Print ISBN: 978-3-319-51132-0

Electronic ISBN: 978-3-319-51133-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-51133-7_15

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