Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

Abstract

A quadcopter is a rotorcraft with a simple mechanical construction. It has the same hovering capability similar to the traditional helicopter, but it is easier to maintain. The quadcopter is hard to control due to its unstable system with highly coupled and non-linear dynamics. In order to design a robust control algorithm, it is crucial to obtain a precise quadrotor flight dynamics through system identification approach. System identification is a 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 and presents a comprehensive survey of the modeling techniques used to determine the flight dynamics of a quadrotor with a particular focus on NN based system identification method. The presented research works have been classified into different categories such as the first principle modeling, system identification and implementation of NN based system identification in quadcopter platform. Finally, the paper highlights challenges that need to be addressed in developing efficient NN based system identification model for unmanned quadcopter system.

Identificación No Paramétrica De Un Brazo Robótico De 2 GDL Utilizando Redes Neuronales RBF

“…Se ha planteado la utilización de redes RBF, puesto que estas han probado ser aproximadores universales, es decir, capaces de aproximar muchos comportamientos dinámicos, obteniéndose una respuesta con estas superior a las obtenidas con otro tipo de red, como las típicas Perceptrón de Capa Múltiple (MLP, en inglés) (Pairan et al 2020).…”