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Neural Processing Letters

Erschienen in:

26.07.2019

Data-Based Online Optimal Temperature Tracking Control in Continuous Microwave Heating System by Adaptive Dynamic Programming

verfasst von: Tong Liu, Shan Liang, Qingyu Xiong, Kai Wang

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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Abstract

Control of continuous microwave heating system (CMHS) is truly a complex problem with time variance, uncertainty and nonlinearity, which becomes prohibitive to use a conventional model-based approach. To overcome this, a novel data-based optimal temperature tracking control is designed for CMHS in this paper. In order to obtain the complex dynamics of CMHS, a neural network model is first constructed driven by process data. After transforming the original temperature tracking problem into an error regulation problem, adaptive dynamic programming is introduced to deal with the regulation problem as well as to decrease operation cost. The design and operation of this controller depend mainly on the online data, and minor prior knowledge is required. Simulation results show that the proposed method can effectively control the CMHS in terms of temperature tracking and energy utilization.

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Titel: Data-Based Online Optimal Temperature Tracking Control in Continuous Microwave Heating System by Adaptive Dynamic Programming
verfasst von: Tong Liu
Shan Liang
Qingyu Xiong
Kai Wang
Publikationsdatum: 26.07.2019
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-019-10081-1

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