Recursive fast orthogonal search for real-time adaptive modelling of a quadcopter

摘要

This paper presents a novel application of Recursive Fast Orthogonal Search (R-FOS) to develop a timevarying, linear, state-space model approximating the dynamics of a quadcopter. The algorithm is successfully executed in real-time at a rate of 1000Hz using a simulated quadcopter testbed. The performance of the linear models is evaluated in terms of Accumulated Mean Squared error (AMSE) over finite prediction horizons. A significant decrease in AMSE is observed with more frequent model updates, particularly during aggressive maneuvers. This demonstrates the real-time adaption of the models to various flight regimes.