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

TL;DR

This paper develops and tests two nonlinear model predictive control algorithms for 3D path-following of small fixed-wing UAVs, demonstrating improved real-world performance over traditional guidance methods.

Contribution

Introduces two novel MPC-based 3D path-following guidance algorithms specifically designed for small fixed-wing UAVs, with real-world flight validation.

Findings

MPC algorithms outperform baseline lookahead guidance in tests

Successful flight tests at speeds up to 36 m/s

Demonstrates feasibility of nonlinear MPC for complex 3D paths

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.