Mach number control of continuous wind tunnel based on Gaussian process regression

Maintaining the stability of Mach number plays an important role in the success of wind tunnel experiments. The traditional PID feedback control algorithm has a certain time lag and can not obtain the required control precision of Mach number in the experiments with continuously changing angle of attack. To overcome this defect, a feed forward strategy based on Gaussian process regression is proposed. The strategy combines the PID controller to carry out the control of Mach number. Firstly, the cleaned data is normalized in addition to cleaning the raw data to reduce the effects of noise. Secondly, the Gaussian process regression is employed to model the relationship between the inputs (angle of attack, real-time Mach number, and cross-sectional area) and the output (rotational speed of compressor) by two different strategies, which randomly divide data set and group data set by Mach number. The trained model is compared with other commonly-used approaches in terms of predictive accuracy. Finally, an improved PID control strategy that leverages the prediction and confidence results of Gaussian process regression is proposed. The experimental results confirm that Gaussian process regression has satisfactory ability of modelling wind tunnel data, and the control strategy that combines the PID with the feed forward control based on Gaussian process regression can improve the control precision of Mach number with continuous variable angles of attack in wind tunnel.