### Adaptive Neural Network Control of Thermoacoustic Instability in Rijke Tube: A Fully Actuated System Approach

ZHAO Yuzhuo1, MA Dan1, MA Hongwei2

1. 1. State Key Laboratory of Synthetical Automation for Process Industries and College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
• Received:2022-01-17 Revised:2022-03-01 Published:2022-04-13
• Supported by:
This research was supported by the National Natural Science Foundation of China under Grant No. 61973060 and the Science Center Program of National Natural Science Foundation of China under Grant No. 62188101.

ZHAO Yuzhuo, MA Dan, MA Hongwei. Adaptive Neural Network Control of Thermoacoustic Instability in Rijke Tube: A Fully Actuated System Approach[J]. Journal of Systems Science and Complexity, 2022, 35(2): 586-603.

Thermoacoustic instability phenomena often encounter in gas turbine combustors, especially for the premixed combustor design, with many possible detrimental results. As a classical experiment, the Rijke tube is the simplest and the most effective illustration to study the thermoacoustic instability. This paper investigates the active control approach of the thermoacoustic instability in a horizontal Rijke tube. What’s more, the radial basis function (RBF) neural network is adopted to estimate the complex unknown continuous nonlinear heat release rate in the Rijke tube. Then, based on the proposed second-order fully actuated system model, the authors present an adaptive neural network controller to guarantee the flow velocity fluctuation and pressure fluctuation to converge to a small region of the origin. Finally, simulation results demonstrate the feasibility of the design method.
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