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

TL;DR

This paper introduces a neural network-based in-situ thrust monitoring and adaptive regulation framework for solid fuel ramjets, enhancing control accuracy and operational stability through learning algorithms and real-time data processing.

Contribution

It presents a novel integrated neural network and adaptive control system for real-time thrust regulation in solid fuel ramjets, addressing complex combustion dynamics.

Findings

Effective thrust regulation demonstrated in simulations

Neural network accurately estimates thrust from in-situ data

Adaptive controller maintains stable thrust despite variable conditions

Abstract

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.