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

In modern era of aviation technology evolution, unmanned aerial vehicles have proved to be crucial in all fields including military and research. The development of robust control system and successful mission accomplishment requires an meticulous UAV model. The aim of this paper is to lay out an elaborate model estimation scheme using various model structure techniques including Auto-regressive Exogenous, Auto-regressive Moving Average exogenous, Box Jenkin’s, Output Error, and state space and non-linear Auto-regressive Exogenous. The obtained models are then analyzed and compared for best model quality parameters like residual analysis, final prediction error and fit percentages. The efficacy of developed model through proposed model is further validated using simulations data for UAV. Results demonstrate the model’s feasibility as it properly predicts system performance over a broad variety of operating situations. To the best of our knowledge, this is the first time in the literature that a model estimation study for UAV platform with such a wide variety of model structures has been presented.