Leshikar, Christopher (Texas A&M University), Ninan, Nidhin (Texas A&M University), Eves, Kameron (Texas A&M University), Valasek, John (Texas A&M University)

Asymmetric Quadrotor Modeling and State-Space System Identification

Scheduled for presentation during the Regular Session "Technology Challenges" (FrC4), Friday, June 18, 2021, 14:40−15:00, Naoussa

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

This paper synthesizes an analytical nonlinear parametric state-space model of an asymmetric quadrotor Unmanned Air System, and validates the model using a non-parametric model synthesized from measured inputs and outputs from flight logs of the actual vehicle. The offline system identification process is conducted using the Observer Kalman Identification algorithm, which produces a linear discrete-time state-space model. This model is then converted to a continuous time model for comparison to the linearized analytical model. Several dynamic modes, which are difficult to model, are given as motivation for the system identification. The Developmental Flight Test Instrumentation 2, a custom flight test instrumentation system is used for data logging and is designed to run on an Ardupilot autopilot stack. This software is released as open-source. Results presented in the paper demonstrate that the linear model,synthesized using system identification, compares well to the linearized analytical model.