UAV Parameter Estimation Through Machine Learning

Abstract

Parameter identification of Unmanned Aerial Vehicles (UAV) is very helpful for understanding cause-effect relationships of physical phenomenon, investigating system performance and characteristics, fault diagnostics, control development/tuning, and more. Traditional ways of performing parameter identification involve establishing a mathematical model that describes the system’s behavior. This process requires knowledge of the physics involved, careful aircraft instrumentation, and special flight maneuvers for thorough excitation of the flight dynamics involved. The purpose of this paper is to show the application of an equation-less identification of a UAV model method using machine learning. The machine learning algorithm is trained with a set of simulation flight data that incorporates variations in the parameters to be identified. To achieve autonomous and consistent flights, a Software-In-the-Loop (SIL) simulation is constructed between X-Plane and Mission Planner. Several machine learning regression models are explored including linear regression, regression trees, gaussian process regression, support vector machine and ensembles of regression trees.