Grey-box modelling of an Unmanned Quadcopter during Aggressive Maneuvers

The treatment of quadcopter dynamics around steady-state conditions has often ignored some rotorcraft aerodynamic effects due to its complicated physical modeling or black-box estimated model. The identification of an unmanned quadcopter in accelerated flight using a grey-box modeling approach is investigated. The classical approach of using either first-principles modeling (white-box modeling) or pure observations modeling (black-box modeling) have limitations particularly for real-time applications. Radial basis functions neural networks (RBF-NN) were used to estimate the rotor dynamics parameters (motor PWM outputs) from an unknown flapping dynamics model. The identified models shows that a RBF-based grey-box modeling approach specifically in aggressive maneuvers, has benefits in both modeling accuracy, network size and robustness to noise.