Learning-Enabled Robust Control of Thermoacoustic Oscillations

This work addresses the learning-enabled robust control of thermoacoustic systems under uncertainties and unknown disturbances. A widely recognized thermoacoustic system model is considered, incorporating monopole-like actuators and sensor arrays for demonstration. The model is reformulated into a set of ordinary differential equations (ODEs) suitable for control design. The learning-enabled control law is derived by minimizing a cost function that reflects the target closed-loop error dynamics using a gradient descent method. The closed-loop system stability is established through a pseudolinear system approach. The learning-enabled control law effectively mitigates the unwanted effects of uncertainties and disturbances. The controller performance is validated using a combustion system model featuring two modes and two actuators.