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Acceleration-Based Control of Fixed-Wing UAVs for Guidance Applications

Jixiang Wang, Siyuan Yang, Ziyi Wu, Siqi Wei, Ashay Wakode, Agata Barcis, Hung Nguyen, Shaoming He

Abstract

Acceleration-commanded guidance laws (e.g., proportional navigation) are attractive for high-level decision making, but their direct deployment on fixed-wing UAVs is challenging because accelerations are not directly actuated and must be realized through attitude and thrust under flight-envelope constraints. This paper presents an acceleration-level outer-loop control framework that converts commanded tangential and normal accelerations into executable body-rate and normalized thrust commands compatible with mainstream autopilots (e.g., PX4/APM). For the normal channel, we derive an engineering mapping from the desired normal acceleration to roll- and pitch-rate commands that regulate the direction and magnitude of the lift vector under small-angle assumptions. For the tangential channel, we introduce an energy-based formulation inspired by total energy control and identify an empirical thrust-energy acceleration relationship directly from flight data, avoiding explicit propulsion modeling or thrust bench calibration. We further discuss priority handling between normal and tangential accelerations under saturation and non-level maneuvers. Extensive real-flight experiments on a VTOL fixed-wing platform demonstrate accurate acceleration tracking and enable practical implementation of proportional navigation using only body-rate and normalized thrust interfaces.

Acceleration-Based Control of Fixed-Wing UAVs for Guidance Applications

Abstract

Acceleration-commanded guidance laws (e.g., proportional navigation) are attractive for high-level decision making, but their direct deployment on fixed-wing UAVs is challenging because accelerations are not directly actuated and must be realized through attitude and thrust under flight-envelope constraints. This paper presents an acceleration-level outer-loop control framework that converts commanded tangential and normal accelerations into executable body-rate and normalized thrust commands compatible with mainstream autopilots (e.g., PX4/APM). For the normal channel, we derive an engineering mapping from the desired normal acceleration to roll- and pitch-rate commands that regulate the direction and magnitude of the lift vector under small-angle assumptions. For the tangential channel, we introduce an energy-based formulation inspired by total energy control and identify an empirical thrust-energy acceleration relationship directly from flight data, avoiding explicit propulsion modeling or thrust bench calibration. We further discuss priority handling between normal and tangential accelerations under saturation and non-level maneuvers. Extensive real-flight experiments on a VTOL fixed-wing platform demonstrate accurate acceleration tracking and enable practical implementation of proportional navigation using only body-rate and normalized thrust interfaces.
Paper Structure (16 sections, 29 equations, 5 figures, 2 tables)

This paper contains 16 sections, 29 equations, 5 figures, 2 tables.

Figures (5)

  • Figure 1: SkyFury VTOL fixed-wing Platform.
  • Figure 2: Relationship between $V_a^2$ and $a_{TE}$ at $T_c = 0.7$.
  • Figure 3: Relationship between $a_{TE}$ and $T_c$ at $V_a = 20\,\mathrm{m/s}$.
  • Figure 4: Results of the coupled normal--tangential acceleration response experiment. (a)--(c) Command and measured body-frame accelerations along the $x$, $y$, and $z$ axes. The light-blue curves denote raw measured signals, while the dark-blue curves represent moving-average filtered data over a 10-sample window. The black dash-dotted curves show the corresponding responses obtained from Gazebo simulation under the same command sequence. (d) Ground speed and normalized thrust command $T_c$.
  • Figure 5: Results of the proportional navigation flight experiment. (a) Flight trajectory and virtual target point. Miss distance: 0.58 m. (b)--(d) Command and measured body-frame accelerations along the $x$, $y$, and $z$ axes. Light-blue curves represent raw measured signals, and dark-blue curves show moving-average filtered data over a 10-sample window.