Stabilized Robust Control for Lightweight Autonomous Aircraft Mobility: A Quantum Reinforcement Learning Approach

The stability of aircraft remains vulnerable to sudden external disturbances and unpredictable vortices. The aircraft's attitude angles undergo rapid changes due to random turbulence. Consequently, to ensure safety, it is essential to control the aircraft's control surfaces, i.e., ailerons, elevators, and rudder angles, to maintain its static stability. Although classical closed-loop control methods have been widely adopted, their limited adaptability to changing dynamics calls for more robust solutions. Reinforcement learning (RL) offers adaptive capabilities but often demands a large number of training parameters and substantial computational resources, which may be impractical for real-time lightweight aircraft applications. To overcome these limitations, this paper introduces a quantum aircraft with the quantum actorcritic networks-based aircraft control (QACN-AC) algorithm. By utilizing quantum neural networks (QNN), QACN-AC significantly reduces the number of parameters required for training, thus mitigating computational overhead while preserving robust control performance. The QACN-AC's effectiveness is validated through realistic simulations leveraging Boeing's B777 specifications. The results highlight QACN-AC's superiority over conventional RL, evidenced by a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.25 \times$</tex> higher control performance and a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$760 \times$</tex> reduction in the number of required parameters.