Abstract
This paper presents the application of a Grey Box Neural Model (GNM) in adaptive-predictive control of the combustion chamber temperature of a pilot-scale vibrating fluidized dryer. The GNM is based upon a phenomenological model of the process and a neural network that estimates uncertain parameters. The GNM was synthesized considering the energy balance and a radial basis function neural network (RBF) trained on-line to estimate heat losses. This predictive model was then incorporated into a predictive control strategy with one step look-ahead. The proposed system shows excellent results with regard to adaptability, predictability and control when subject to setpoint and disturbances changes.
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© 2007 Springer-Verlag Berlin Heidelberg
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Cubillos, F.A., Acuña, G. (2007). Adaptive Control Using a Grey Box Neural Model: An Experimental Application. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_37
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DOI: https://doi.org/10.1007/978-3-540-72383-7_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
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