PH optimal control in the clarifying process of sugar cane juice based on DHP

Authors:
Xiaofeng Lin;Qianli Teng;Chunning Song;Shaojian Song;Huixia Liu
Affiliations:
School of Electrical Engineering, Guangxi University, Nanning, Guangxi, China;School of Electrical Engineering, Guangxi University, Nanning, Guangxi, China;School of Electrical Engineering, Guangxi University, Nanning, Guangxi, China;School of Electrical Engineering, Guangxi University, Nanning, Guangxi, China;School of Light Industry and Food Engineering, Guangxi University, Nanning, Guangxi, China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

This paper proposes the use of error back proragation (BP) neural network to efficiently control the pH in the clarifying process of sugar cane juice. In particular approximate dynamic programming (ADP) is implemented to solve this nonlinear control problem. The neural network model of the clarifying process of sugar cane juice and a neural network controller based on the idea of ADP to achieve optimal control are developed. The strategy and training procedures of dual heuristic programming (DHP) are discussed. The result is the "plant" has been effectively controlled using DHP.