3D Path-Following Guidance via Nonlinear Model Predictive Control for Fixed-Wing Small UAS

TL;DR

Problem: high-performance 3D path-following for fixed-wing sUAS in wind. Approach: two nonlinear MPC formulations CR-MPC and MPCC based on a control-augmented RAAVEN model. Key results: field tests over four challenging 3D paths show lower path error and higher airspeed/ground speed than a lookahead baseline, with onboard execution at up to $V_g \approx 36$ m/s. Significance: demonstrates real-time, high‑fidelity path-following guidance on embedded hardware, enabling faster, safer operations for time‑critical missions.

Abstract

This paper presents the design, implementation, and flight test results of two novel 3D path-following guidance algorithms based on nonlinear model predictive control (MPC), with specific application to fixed-wing small uncrewed aircraft systems. To enable MPC, control-augmented modelling and system identification of the RAAVEN small uncrewed aircraft is presented. Two formulations of MPC are then showcased. The first schedules a static reference path rate over the MPC horizon, incentivizing a constant inertial speed. The second, with inspiration from model predictive contouring control, dynamically optimizes for the reference path rate over the controller horizon as the system operates. This allows for a weighted tradeoff between path progression and distance from path, two competing objectives in path-following guidance. Both controllers are formulated to operate over general smooth 3D arc-length parameterized curves. The MPC guidance algorithms are flown over several high-curvature test paths, with comparison to a baseline lookahead guidance law. The results showcase the real-world feasibility and superior performance of nonlinear MPC for 3D path-following guidance at ground speeds up to 36 meters per second.

Figures (14)

• Figure 1: A RAAVEN aircraft, outfitted with an array of atmospheric sensors, conducting a low-pass maneuver during a scientific field campaign.
• Figure 2: Angle definitions with respect to the aircraft body b and inertial i frame assuming side-slip angle $\beta = 0$. $\mathbf{V}_a$ and $\mathbf{V}_g$ are the air-relative and ground velocity vectors, respectively.
• Figure 3: Control inputs during free-form validation flight.
• Figure 4: Control-augmented aircraft model output (red) on validation flight data (grey).
• Figure 5: MPCC solve times over OCP horizon lengths during hardware-in-the-loop experiments. An OCP horizon of $T_f = 5$s was chosen to maximize closed-loop performance while maintaining low feedback delays for operation at 10 Hz.