Neural Network Based System Identification for Quadcopter Dynamic Modelling: A Review

Abstract

A quadcopter is a rotorcraft with simple mechanical construction. It has the same hovering capability, similar to the traditional helicopter, but it is easier to maintain.  The quadcopter is very difficult to control due to its unstable system with highly coupled and non-linear dynamics. To design robust control algorithms, it is crucial to obtain precise quadrotor flight dynamics through system identification, which is a new method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter, the first principle modeling, system identification of quadcopter, and implementation of NN based system identification in quadcopter platform.