Jardine, Peter Travis (Royal Military College of Canada), Givigi, Sidney (Royal Military College of Canada), Yousefi, Shahram (Queen's University), Korenberg, Michael (Queen's University)

Recursive Fast Orthogonal Search for Real-Time Adaptive Modelling of a Quadcopter

Scheduled for presentation during the "UAS Applications - IV" (ThA4), Thursday, June 15, 2017, 11:00−11:20, Lummus Island

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

This paper presents a novel application of Recursive Fast Orthogonal Search (R-FOS) to develop a time-varying, 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.