Abstract
This paper applied a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Marquardt (L-M) learning algorithm capable to estimate parameters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural control schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.
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
Narendra, K.S., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Transactions on Neural Networks 1(1), 4–27 (1990)
Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J.: Neural Network for Control Systems (A survey). Automatica 28, 1083–1112 (1992)
Haykin, S.: Neural Networks, a Comprehensive Foundation, 2nd edn., Section 2.13, pp. 84–89, Section 4.13, pp. 208-213. Prentice-Hall, Upper Saddle River (1999)
Chen, S., Billings, S.A.: Neural Networks for Nonlinear Dynamics System Modeling and Identification. International Journal of Control 56, 319–346 (1992)
Boskovic, J.D., Narendra, K.S.: Comparison of Linear, Nonlinear and Neural – Network - Based Adaptive Controllers for a Class of Fed - Batch Fermentation Processes. Automatica 31, 817–840 (1995)
Ku, C.C., Lee, K.Y.: Diagonal Recurrent Neural Networks for Dynamic Systems Control. IEEE Transactions on Neural Networks 6(1), 144–156 (1995)
Jin, L., Gupta, M.: Stable Dynamic Backpropagation Learning in Recurrent Neural Networks. IEEE Transactions on Neural Networks 10, 1321–1334 (1999)
Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition. Springer, Germany (2005)
Mastorocostas, P.A., Theocharis, J.B.: A Stable Learning Algorithm for Block - Diagonal Recurrent Neural Networks: Application to the Analysis of Lung Sounds. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 36(2), 242–254 (2006)
Kazemy, A., Hosseini, S.A., Farrokhi, M.: Second Order Diagonal Recurrent Neural Network. In: Proceedings of the IEEE International Symposium on Industrial Electronics, ISIE, Vigo, Spain, June 2007, pp. 251–256. IEEE Inc., New York (2007)
Sage, A.P.: Optimum Systems Control. In: Library of Congress Catalog Number, pp. 68–20862. Prentice-Hall Inc., Englewood Cliffs (1968)
Baruch, I.S., Mariaca-Gaspar, C.R.: A Levenberg-Marquardt Learning Applied for Recurrent Neural Identification and Control of a Wastewater Treatment Bioprocess. International Journal of Intelligent Systems 24, 1094–1114 (2009)
Baruch, I.S., Mariaca-Gaspar, C.R., Barrera-Cortes, J.: Recurrent Neural Network Iden-tification and Adaptive Neural Control of Hydrocarbon Biodegradation Processes. In: Hu, X., Balasubramaniam, P. (eds.) Recurrent Neural Networks, Ch. 4, pp. 61–88. I-Tech Education and Publishing KG, Vienna (2008) ISBN 978-953-7619-08-4
Mariaca Gaspar, C.R.: Topologies, Learning and Stability of Hybrid Neural Networks, Applied for Nonlinear Biotechnological Processes, Ph. D. Thesis (in spanish), Baruch, I.S., Martinez-Garcia, J.C (thesis directors), Department of Automatic Control, CINVESTAV-IPN, Mexico City, 3 (July 2009)
Ngia, L.S., Sjöberg, J.: Efficient Training of Neural Nets for Nonlinear Adaptive Filter-ing Using a Recursive Levenberg Marquardt Algorithm. IEEE Trans. on Signal Processing 48, 1915–1927 (2000)
Zhang, T., Guay, M.: Adaptive Nonlinear Control of Continuously Stirred Tank Reactor Systems. In: Proceedings of the American Control Conference, Arlington, June 25-27, pp. 1274–1279 (2001)
Lightbody, G., Irwin, G.W.: Nonlinear Control Structures Based on Embedded Neural System Models. IEEE Trans. on Neural Networks 8, 553–557 (1997)
Wan, E., Beaufays, F.: Diagrammatic Method for Deriving and Relating Temporal Neural Network Algorithms. Neural Computations 8, 182–201 (1996)
Young, K.D., Utkin, V.I., Ozguner, U.: A Control Engineer’s Guide to Sliding Mode Control. IEEE Transactions on Control Systems Technology 7(3), 328–342 (1999)
Levent, A.: Higher Order Sliding Modes, Differentiation and Output Feedback Control. Fridman, L.M.(guest ed.) International Journal of Control, Special Issue Dedicated to Vadim Utkin on the Occasion of his 65th Birthday 9/10 (June 15-July 10, 2003) ISSN 0020-7179
Eduards, C., Spurgeon, S.K., Hebden, R.G.: On the Design of Sliding Mode Output Feedback Controllers. Fridman, L.M.(guest ed.) International Journal of Control, Special Issue Dedicated to Vadim Utkin on the Occasion of his 65th Birthday 76(9/10), 893–905 (June 15-July10, 2003) ISSN 0020-7179
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
Baruch, I., Mariaca-Gaspar, CR., Barrera-Cortes, J., Castillo, O. (2010). Direct and Indirect Neural Identification and Control of a Continuous Bioprocess via Marquardt Learning. In: Castillo, O., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Intelligent Control and Mobile Robotics. Studies in Computational Intelligence, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15534-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-15534-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15533-8
Online ISBN: 978-3-642-15534-5
eBook Packages: EngineeringEngineering (R0)