Active Control of Thermoacoustic Instability in a Model Combustor with Neuromorphic Evolvable Hardware

Continuous Time Recurrent Neural Networks (CTRNNs) have previously been proposed as an enabling paradigm for evolving analog electrical circuits to serve as controllers for physical devices [6]. Currently underway is the design of a CTRNN-EH VLSI chips that combines an evolutionary algorithm and a reconfigurable analog CTRNN into a single hardware device capable of learning control laws of physical devices. One potential application of this proposed device is the control and suppression of potentially damaging thermoacoustic instability in gas turbine engines. In this paper, we will present experimental evidence demonstrating the feasibility of CTRNN-EH chips for this application. We will compare our controller efficacy with that of a more traditional Linear Quadratic Regulator (LQR), showing that our evolved controllers consistently perform better and possess better generalization abilities. We will conclude with a discussion of the implications of our findings and plans for future work.