ICUAS'17 Paper Abstract


Paper ThA4.4

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 12, 2021

Keywords UAS Applications, Autonomy, Control Architectures


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.



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