arXiv subjects

Ryan DeBoskey

Publications and source records attributed to Ryan DeBoskey.

2 recordsLinked to original sources

arXiv2026

Adaptive Thrust Regulation in Solid-fuel Ramjet with Variable Geometry Inlet

This paper presents the application of a novel data-driven adaptive control technique, dynamic mode adaptive control (DMAC), to regulate thrust in a solid-fuel ramjet (SFRJ). A quasi-static one-dimensional model of SFRJ with a variable geometry inlet is developed to compute thrust. An adaptive tracking controller is then designed using the DMAC framework, which leverages dynamic mode decomposition to approximate the local system behavior, followed by a tracking controller designed around the identified model. Simulation results demonstrate that DMAC achieves accurate thrust regulation across a range of commanded profiles and operating conditions, without requiring an analytical model of the SFRJ. These findings indicate that DMAC provides a reliable and effective approach for model-free thrust regulation in an SFRJ with variable-geometry inlets as the control input.

math.OC↗

arXiv2025

An In-situ Solid Fuel Ramjet Thrust Monitoring and Regulation Framework Using Neural Networks and Adaptive Control

Controlling the complex combustion dynamics within solid fuel ramjets (SFRJs) remains a critical challenge limiting deployment at scale. This paper proposes the use of a neural network model to process in-situ measurements for monitoring and regulating SFRJ thrust with a learning-based adaptive controller. A neural network is trained to estimate thrust from synthetic data generated by a feed-forward quasi-one-dimensional SFRJ model with variable inlet control. An online learning controller based on retrospective cost optimization is integrated with the quasi-one-dimensional SFRJ model to regulate the thrust. Sensitivity studies are conducted on both the neural network and adaptive controller to identify optimal hyperparameters. Numerical simulation results indicate that the combined neural network and learning control framework can effectively regulate the thrust produced by the SFRJ model using limited in-situ data.

math.OC↗