Neural network PID control for combustion instability

Abstract

Considerable research studies have been reported on developing effective active control means to mitigate combustion instability. In this work, a neural network PID (NN-PID) controller is proposed and demonstrated to suppress the oscillating pressure in a cylindrical Rijke tube, for which the combustion instability is modelled by a 1D acoustic network model together with linear and nonlinear flame models. The flame model is based on the classical n − τ model filtered by a first-order low pass filter. The nonlinearity of the flame model is realised using a gain function saturated with velocity perturbation. A fuel valve and a loudspeaker are adopted as two independent control actuators. Results show that system oscillation begins to attenuate when the NN-PID control starts. The attenuation rate using a fuel valve as the actuator is faster than that using a loudspeaker. For nonlinear combustion oscillations, the NN-PID controller could inhibit the reference pressure oscillation in the linear growth and saturation processes. A maximum overshoot of 2.3% could occur when the loudspeaker is used as the actuator to suppress system oscillation. The NN-PID controller is not affected by noise with a fuel valve or the loudspeaker and can effectively suppress and eliminate the system pressure oscillation in different oscillating stages with different flame models.