NDLI: Decentralized neural network variable structure controller design for wood drying process

Content Provider	IEEE Xplore Digital Library
Author	Jun Cao Liangkuan Zhu Qinglei Hu
Copyright Year	2010
Description	Author affiliation: School of Electromechanical Engineering, Northeast Forestry University, Harbin, 150040, China (Jun Cao; Liangkuan Zhu; Qinglei Hu) This paper investigates the development and evaluation of a robust control system for a wood drying kiln process incorporating decentralized variable structure control (DVSC) such that the moisture content of lumber will reach and be stabilized at the desired set point. A description of the dynamics of the wood drying process by means of the time-delay neural network is also presented, in which the back-propagation algorithm was implemented for testing, training and validation. Then this identified model is used for simulation purpose and controller design. For comparison purpose, a conventional proportional-integral-derivative (PID) controller is also employed and system performance is evaluated through simulations. The results are evaluated to tune the controller parameters to achieve good performance in the wood-drying kiln and the DVSC strategy promises improved performance. The control system developed in this study may be applied in industrial wood-drying kilns, with a clear potential for improved quality and increased speed of drying.
Starting Page	4477
Ending Page	4482
File Size	220203
Page Count	6
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498324
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kilns Three-term control Decentralized Neural Variable Structure Control Temperature-Moisture Control Control systems Robust control Moisture control Proportional control System performance Wood Drying Kiln Neural networks Pi control Testing
Content Type	Text
Resource Type	Article

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