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Hypernetwork-Conditioned Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Actuator Failures

Dennis Marquis, Mazen Farhood

Abstract

This paper presents a reinforcement learning-based path-following controller for a fixed-wing small uncrewed aircraft system (sUAS) that is robust to certain actuator failures. The controller is conditioned on a parameterization of actuator faults using hypernetwork-based adaptation. We consider parameter-efficient formulations based on Feature-wise Linear Modulation (FiLM) and Low-Rank Adaptation (LoRA), trained using proximal policy optimization. We demonstrate that hypernetwork-conditioned policies can improve robustness compared to standard multilayer perceptron policies. In particular, hypernetwork-conditioned policies generalize effectively to time-varying actuator failure modes not encountered during training. The approach is validated through high-fidelity simulations, using a realistic six-degree-of-freedom fixed-wing aircraft model.

Hypernetwork-Conditioned Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Actuator Failures

Abstract

This paper presents a reinforcement learning-based path-following controller for a fixed-wing small uncrewed aircraft system (sUAS) that is robust to certain actuator failures. The controller is conditioned on a parameterization of actuator faults using hypernetwork-based adaptation. We consider parameter-efficient formulations based on Feature-wise Linear Modulation (FiLM) and Low-Rank Adaptation (LoRA), trained using proximal policy optimization. We demonstrate that hypernetwork-conditioned policies can improve robustness compared to standard multilayer perceptron policies. In particular, hypernetwork-conditioned policies generalize effectively to time-varying actuator failure modes not encountered during training. The approach is validated through high-fidelity simulations, using a realistic six-degree-of-freedom fixed-wing aircraft model.

Paper Structure

This paper contains 21 sections, 11 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Hypernetwork-Based Policy Adaptation
  4. sUAS Dynamics
  5. Learning Framework
  6. Simulation Environment
  7. Failure Parameterization
  8. Action and Observation Spaces
  9. Reward Design
  10. Reference Path Generation
  11. Experimental Setup
  12. Controller Architectures
  13. Training Configuration
  14. Evaluation Protocol
  15. Results
  16. ...and 6 more sections

Figures (4)

  • Figure E1: Average MaxPE across failure magnitudes for each actuator. Top: static failures. Bottom: flutter failures.
  • Figure E2: Example rudder flutter signal from a simulation episode. This type of conditioning signal is used to assess generalization to unseen, nonstationary failures.
  • Figure E3: State and control histories for a MLP WC episode under rudder flutter, compared against the Film + HC policy.
  • Figure E4: State and control histories for a FilM + HC WC episode under rudder flutter, compared against the MLP policy.

Theorems & Definitions (1)

  • Remark 1