An Adaptive Neuromorphic Chip for Combustion Control

Continuous Time Recurrent Neural Networks (CTRNNs) have previously been proposed
as an enabling control technology for mechanical devices. Currently, we are in the advanced
stages of designing custom VLSI chips that combine automated learning and analog
CTRNNs into unified hardware devices capable of learning control laws for physical
systems. The chip’s self-configuring capability is potentially useful for the control of
combustion systems. In this paper, we will discuss the underlying technology and examine
preliminary simulation experiments in which our device successfully learned to suppress
instability in a bench top combustor. The paper will conclude with a discussion of expected
future work.