Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic model. This paper aims to present consolidated framework the estimation an experimental UAV utilizing flight data. An elaborate mechanism proposed model structures, such as Autoregressive Exogenous (ARX), Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), state-space non-linear Exogenous. A perspective analysis comparison made identify salient aspects each structure. Model configuration with best characteristics then identified based upon quality parameters residual analysis, final prediction error, fit percentages. Extensive validation evaluate performance developed performed dynamics data collected. Results indicate model’s viability can accurately predict at wide range operating conditions. Through this, our knowledge, we first time utilizes comprehensive instead simulation work.