NDLI: Adaptive neural network controller for the flush material Belt Weigh Feeder

Content Provider	IEEE Xplore Digital Library
Author	Tsung-Ying Sun Ming-Chin Yang Shang-Jeng Tsai Jyun-Sian He
Copyright Year	2009
Description	Author affiliation: Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan, R.O.C. (Tsung-Ying Sun; Ming-Chin Yang; Shang-Jeng Tsai; Jyun-Sian He)
Abstract	The flush material Belt Weigh Feeder (BWF) is used in many material handling plants. The stability and the performance of the layer control system will affect the quality of the production. In general, the behavior of the flush material on the BWF is non-linear, time-lag, and disturbance character. The layer of the flush material on the belt is hard to be stably controlled especially the occurrence of unstable situation in flush material or the Prefeeder feeding rate. This paper adopted two adaptive neural network (ANN) controller in the general PID control system to improve the performance. The proposed ANN controllers are utilized instead of the original PID controllers to deal with the problems of the unstable pheomena. This paper offers the simulation result of the material character (coarse size, moisture content) or the Prefeeder feeding rate stable and unstable condition (involve the Interference Signal) for the original and the proposed control system. The simulation results are distinct better than the original control system. The variations of the material character (coarse size, moisture content) or the Prefeeder feeding rate can be successfully predicted by the ANN controllers and adjusted the Proportional Actuator immediately. Finally the proposed control system remains convergence with smooth and stable. Therefore, the accuracy, stability and the performance of the control system are improved.
Starting Page	3018
Ending Page	3023
File Size	11815369
Page Count	6
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178814
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Programmable control Adaptive control Neural networks Belts Control systems Artificial neural networks Stability Three-term control Moisture
Content Type	Text
Resource Type	Article

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