NDLI: Self-adaptive RBF neural network PID control in exhaust temperature of micro gas turbine

Content Provider	IEEE Xplore Digital Library
Author	Jiang-Jiang Wang Chun-Fa Zhang You-Yin Jing
Copyright Year	2008
Description	Author affiliation: Sch. of Energy & Power Eng., North China Electr. Power Univ., Baoding (Jiang-Jiang Wang; Chun-Fa Zhang; You-Yin Jing)
Abstract	Mathematical model of exhaust temperature control in micro gas turbine is introduced. To obtain better performance, a self-adaptive PID control is applied to the exhaust temperature control. The parameters of PID control are tuned by radial basis function (RBF) neural network. In this paper, the RBF neural network is given which has been used extensively in the areas of pattern recognition, systems modeling and identification. The effectiveness and efficiency of the proposed control strategy is demonstrated by applying it to the exhaust temperature control. The simulations show that the dynamic responses of the exhaust control system can be effectively improved and the anti-disturbance of the proposed controller is better than that of the PID controller. However, the learning rate of RBF neural network and PID parameters is not too large due to the great gain of micro gas turbine. Otherwise the output will surge acutely.
Starting Page	2131
Ending Page	2136
File Size	358581
Page Count	6
File Format	PDF
ISBN	9781424420957
DOI	10.1109/ICMLC.2008.4620758
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Turbines Temperature control Biological neural networks Neurons Cogeneration Control systems Micro gas turbine Radial basis function(RBF) Neural network PID control Self-adaptive Exhaust temperature control
Content Type	Text
Resource Type	Article

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