Abstract
In this paper, a neural network (NN) based temperature control scheme is presented for cooling coil of Air Handling Unit (AHU) to overcome the unknown system dynamics and its inherent nonlinearities. First of all, by utilizing the standard thermodynamic formulas, the cooling coil is modeled by a discrete-time uncertain nonlinear system instead of a linear one, in order to accommodate its different behaviors under various cooling loads. Thereafter, a two-layer NN is utilized to approximate the unknown dynamics within the system. The online tuning algorithm of the NN weights is also provided with no offline training needed. Consequently, the identification process of the cooling coil unit is obviated. Theoretical results show the stable tracking performance of the close-loop system, while the effectiveness of this method is demonstrated under simulation environment.
This work was partially supported by the National Natural Science Foundation of China (NSFC) (No. 21076179, 60736021, 61104008), the National High-tech R&D Program of China (863 Program) (No. 2006AA04Z184), the National Basic Research Program of China (973 Program) (No. 2012CB720505), and the Natural Science Foundation of Zhejiang Province (No. Y4080339).
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Zheng, B., Lu, J., Yang, Q., Sun, Y. (2011). A Neural Network Based Temperature Control for Air Handling Unit. In: Tan, H. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25899-2_1
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DOI: https://doi.org/10.1007/978-3-642-25899-2_1
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