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Thrust Regulation in a Solid Fuel Ramjet using Dynamic Mode Adaptive Control

Parham Oveissi, Gohar T. Khokhar, Kyle Hanquist, Ankit Goel

TL;DR

The paper tackles thrust regulation of a solid fuel ramjet (SFRJ) under limited sensing, proposing a data-driven, model-free Dynamic Mode Adaptive Control (DMAC) framework. DMAC blends a dynamic-mode approximation to identify a local linear surrogate with a neural-network-based output mapping and a full-state integral-tracking controller, updated online via a forgetting-factor recursive scheme and input-dither to ensure persistency. The approach is validated on a high-fidelity CFD model of an SFRJ, demonstrating reliable thrust tracking under constant and multi-step commands, with strong robustness to hyperparameter variations. This work offers a practical, mathematically grounded method for robust thrust regulation in uncertain, high-speed propulsion environments, potentially simplifying control design for solid-fuel ramjets.

Abstract

This paper presents the application of a novel data-driven adaptive control technique, called dynamic mode adaptive control (DMAC), for regulating thrust in a solid fuel ramjet (SFRJ). A high-fidelity computational model incorporating compressible flow theory and equilibrium chemistry is used to simulate the combustion dynamics. An adaptive tracking controller is designed using the DMAC framework, which leverages dynamic mode decomposition to approximate the local system behavior, followed by a tracking controller synthesized around the identified model. Simulation results demonstrate that DMAC provides an effective and reliable approach for thrust regulation in SFRJs. In addition, a systematic hyperparameter sensitivity study is conducted by varying the tuning parameters over several orders of magnitude. The resulting responses show that the closed-loop performance and tracking error remain stable across wide parameter variations, indicating that DMAC exhibits strong robustness to hyper parameter tuning.

Thrust Regulation in a Solid Fuel Ramjet using Dynamic Mode Adaptive Control

TL;DR

The paper tackles thrust regulation of a solid fuel ramjet (SFRJ) under limited sensing, proposing a data-driven, model-free Dynamic Mode Adaptive Control (DMAC) framework. DMAC blends a dynamic-mode approximation to identify a local linear surrogate with a neural-network-based output mapping and a full-state integral-tracking controller, updated online via a forgetting-factor recursive scheme and input-dither to ensure persistency. The approach is validated on a high-fidelity CFD model of an SFRJ, demonstrating reliable thrust tracking under constant and multi-step commands, with strong robustness to hyperparameter variations. This work offers a practical, mathematically grounded method for robust thrust regulation in uncertain, high-speed propulsion environments, potentially simplifying control design for solid-fuel ramjets.

Abstract

This paper presents the application of a novel data-driven adaptive control technique, called dynamic mode adaptive control (DMAC), for regulating thrust in a solid fuel ramjet (SFRJ). A high-fidelity computational model incorporating compressible flow theory and equilibrium chemistry is used to simulate the combustion dynamics. An adaptive tracking controller is designed using the DMAC framework, which leverages dynamic mode decomposition to approximate the local system behavior, followed by a tracking controller synthesized around the identified model. Simulation results demonstrate that DMAC provides an effective and reliable approach for thrust regulation in SFRJs. In addition, a systematic hyperparameter sensitivity study is conducted by varying the tuning parameters over several orders of magnitude. The resulting responses show that the closed-loop performance and tracking error remain stable across wide parameter variations, indicating that DMAC exhibits strong robustness to hyper parameter tuning.
Paper Structure (10 sections, 1 theorem, 14 equations, 9 figures)

This paper contains 10 sections, 1 theorem, 14 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Computational Model of SFRJ
  3. Dynamic Mode Adaptive Control
  4. Dynamic Mode Approximation
  5. Tracking Controller
  6. Simulation Results
  7. Command Following
  8. Effect of Hyperparameters
  9. Conclusion
  10. Acknowledgment

Key Result

Proposition III.1

Consider the cost function eq:J_k_def. For all $k\geq 0,$ define the minimizer of eq:J_k_def as Then, the minimizer $\Theta_k$ satisfies where, for all $k \geq 0,$$\gamma_k \stackrel{\triangle}{=} \lambda + \phi_{k-1}^{\rm T} {\mathcal{P}}_{k-1} \phi_{k-1},$ and $\Theta_0 = 0,$${\mathcal{P}}_0 \stackrel{\triangle}{=} R_\Theta^{-1}.$

Figures (9)

  • Figure 1: A typical SFRJ cross section.
  • Figure 2: Truncated and the full SFRJ geometry. Only the truncated section is considered in this work.
  • Figure 3: Mach number contour for truncated SFRJ.
  • Figure 4: Dynamic Mode Adaptive Control (DMAC) architecture for model-free, data-driven, and learning-based control of dynamic systems.
  • Figure 5: Artificial neural network architecture used in this work, consisting of two hidden layers with 10 neurons each and tansig activation functions.
  • ...and 4 more figures

Theorems & Definitions (2)

  • Proposition III.1
  • proof