Abstract
This paper proposes a model predictive control scheme with recurrent fuzzy neural network (RFNN) by using the temperature of the drying process for grain dryers. In this scheme, there are two RFNNs and two PI controllers. One RFNN with feedforeward and feedback connections of grain layer history position states predicts outlet moisture content (MPRFNN), and the other predicts the discharge rate of the dryer (RPRFNN). One PI controller adjusts the objective of the discharge rate by using MPRFNN, and the other adjusts the given frequency of the discharge motor to control the discharge rate of the grain dryer to reach its objective by using RPRFNN. The experiment is carried out by applying the proposed scheme on the control of a gain dryer with four stages to confirm its effectiveness.
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References
Giner, S.A., Bruce, D.M., Mortimore, S.: Two-Dimensional Simulation Model of Steady-state Mixed-flow Grain Drying, Part 1: The Model. J. agric. Engng. Res. 71, 37–50 (1998)
Liu, Q., Bakker-Arkema, F.W.: Automatic Control of Crossflow Grain Dryers, Part 1: Development of a Process Model. J. agric. Engng. Res. 80, 81–86 (2001)
Courtois, F., Nouafo, J.L., Trystram, G.: Control Strategies for Corn Mixed-Flow Dryers. Drying Technology 13, 1153–1165 (1995)
Liu, Q., Bakker-Arkema, F.W.: Automatic Control of Crossflow Grain Dryers, Part 2: Design of a Model-Predictive Controller. J. agric. Engng. Res. 80, 173–181 (2001)
Liu, Q., Bakker-Arkema, F.W.: A Model-Predictive Controller for Grain Drying. J. Food Engineering 49, 321–326 (2001)
Forbes, J.F., Jacobson, B.A., et al.: Model-Based Control Strategies for Commercial Grain Drying Systems. Canadian Journal of Chemical Engineering 62, 773–779 (1984)
Zhang, Q., Litchfield, J.B.: Fuzzy Logic Control for a Continuous Crossflow Grain Dryer. Food Process Engineering 16, 59–77 (1993)
Jover, C., Alastruey, C.F.: Multivarable Control for an Industrial Rotary Dryer. Food Control 17, 653–659 (2006)
Shi, M.H., Wang, X.: Investigation of Moisture Transfer Mechanism in Porous Media During a Rapid Drying Process. Heat Transfer-Asian Research 30, 22–27 (2001)
Zhao, C.Y., Zhao, X.G., Chi, Q.L., Wen, B.C.: Experimental Investigation of the Relation between the Moisture Content of Discharge Grain and the Drying Temperatures of the Maize. Journal of the Chinese Cereal and Oil Association 21, 358–365 (2006)
Zhang, J., Julian Morris, A.: Recurrent Neuro-Fuzzy Networks for Nonlinear Process Modeling. IEEE Trans. Neural Networks 10, 313–326 (1999)
Yu, Y.L., Xu, L.H., Wu, Q.D.: Generalized Fuzzy Networks. Acta Automation Sinca 29, 867–875 (2003)
Yi, F.Z., Hu, Z., Zhou, D.: Fuzzy Controller Parameters Optimization by Using Symbiotic Evolution Algorithm. Electric Machines and Control 7, 54–58 (2003)
Sun, W., Wang, Y.: An Adaptive Control for AC Servo System Using Recurrent Fuzzy Neural Network. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 190–195. Springer, Heidelberg (2005)
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Zhao, C., Chi, Q., Wang, L., Wen, B. (2007). A Model Predictive Control of a Grain Dryer with Four Stages Based on Recurrent Fuzzy Neural Network. 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_5
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DOI: https://doi.org/10.1007/978-3-540-72383-7_5
Publisher Name: Springer, Berlin, Heidelberg
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