Abstract
This work is devoted to present a control application in an industrial process of iron pellet cooking in an important mining company in Brazil. This work employs an adaptive control in order to improve the performance of the conventional controller already installed in the plant. The main strategy approached here is known as Multi-Network-Feedback-Error-Learning (MNFEL). The basic idea in MNFEL is the progressive addition of neural networks in the Feedback-Error-Learning (FEL) scheme. However, this work brings innovation by proposing a mechanism of automatic insertion of new neural networks in MNFEL. In this work, due to the unknown mathematic model of the iron pellet cooking, the plant is simulated by a previously learned neural model. In such simulation environment, the proposed method is compared against conventional PID, FEL and MNFEL.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Almeida Neto, A.: Applications of Multiple Neural Networks in Mechatronic Systems. PhD thesis, Technological Institute of Aeronautics, BR (2003) (in Portuguese)
Almeida Neto, A., Goes, L.C.S., Nascimento Jr., C.L.: Multiple neural networks in flexible link control using feedback-error-learning. In: Proceedings of the 16th Brazilian Congress of Mechanical Engineering (2001)
Almeida Neto, A., Goes, L.C.S., Nascimento Jr., C.L.: Multi-layer feedback-error-learning for control of flexible link. In: Proceedings of the 2nd Thematic Congress of Dynamics, Control and Applications 2, pp. 2281–2289 (2003)
Gomi, H., Kawato, M.: Learning Control for a Closed Loop System Using Feedback-Error-Learning. In: Proceedings of the 29th Conference on Decision and Control, Honolulu, Hawaii, December 1990, vol. 6, pp. 3289–3294 (1990)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1999)
Kawato, M., Furukawa, K., Suzuki, R.: A hierarchical neural-network model for control and learning of voluntary movement. Biol. Cybernetics 57, 169–185 (1987)
Kurosawa, K., Futami, R., Watanabe, T., Hoshimiya, N.: Joint angle control by fes using a feedback error learning controller. IEEE Transactions on Neural Systems & Rehabilitation Engineering 13(3), 359–371 (2005)
Nakanishi, J., Schaal, S.: Feedback error learning and nonlinear adaptive control. Neural Networks 17(10), 1453–1465 (2004)
Ribeiro, P.R.A., Almeida Neto, A., Oliveira, A.C.M.: Using feedback-error-learning for industrial temperature control. In: CACS International Automatic Control Conference (2009)
Ribeiro, P.R.A., Almeida Neto, A., Oliveira, A.C.M.: Multi-network-feedback-error-learning in pelletizing plant control. In: 2nd IEEE International Conference on Advanced Computer Control - ICACC (2010)
Ribeiro, P.R.A., Costa, T.S., Barros, V.H., Almeida Neto, A., Oliveira, A.C.M.: Feedback-error-learning in pelletizing plant control. In: ENIA - 7th Brazilian Meeting on Artificial Intelligence (2009)
Ruan, X., Ding, M., Gong, D., Qiao, J.: On-line adaptive control for inverted pendulum balancing based on feedback-error-learning. Neurocomputing 70(4-6), 770–776 (2007)
Vale. Company, http://www.vale.com (Accessed in: January 10, 2010)
Yiwei, L., Shibo, X.: Neural network and pid hybrid adaptive control for horizontal control of shearer. In: ICARCV, pp. 671–674 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Almeida Ribeiro, P.R., de Almeida Neto, A., de Oliveira, A.C.M. (2010). Multi-Network-Feedback-Error-Learning with Automatic Insertion. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds) Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010). Advances in Intelligent and Soft Computing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13161-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-13161-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13160-8
Online ISBN: 978-3-642-13161-5
eBook Packages: EngineeringEngineering (R0)