Asymmetric Quadrotor Modeling and State-Space System Identification

Abstract

Certain dynamic modes of asymmetric quadrotor configurations are difficult to accurately model analytically. This paper synthesizes an analytical nonlinear parametric state-space model of an asymmetric quadrotor, and verifies it using a non-parametric model calculated from experimentally measured inputs and outputs of the actual vehicle. The offline system identification process produces a discrete-time Linear Time Invariant state-space model using the Observer Kalman Identification algorithm. This model is converted to a continuous time model for comparison to the linearized analytical model. Eigenvlaues, modes, and mode metrics are used to compare the parametric and non-parametric linear models. Results presented in the paper demonstrate that the identified linear model compares well to the linearized analytical model and validates the approach.