Quadrotor Attitude Dynamics Identification Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

Avdeev, Alexander and Assaleh, Khaled and Jaradat, Mohammad A. (2021) Quadrotor Attitude Dynamics Identification Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs. Applied Artificial Intelligence, 35 (4). pp. 265-289. ISSN 0883-9514

[thumbnail of Quadrotor Attitude Dynamics Identification Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs.pdf] Text
Quadrotor Attitude Dynamics Identification Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs.pdf - Published Version

Download (12MB)

Abstract

In the case of quadrotors, system identification is a challenging task because quadrotors are inherently unstable exhibit nonlinear behavior and significant coupling. In addition to this, quadrotors’ behavior is greatly influenced by characteristics and coefficients, which are very hard to measure directly or determine analytically. However, all the difficulties listed above are known to be successfully overcome by the use of artificial intelligence. In this paper, two system identification techniques were applied and compared to model quadrotor attitude dynamics. These techniques are Nonlinear Autoregressive Network with Exogenous Inputs (NARX) and continuous-time transfer function.

Item Type: Article
Subjects: Souths Book > Computer Science
Depositing User: Unnamed user with email support@southsbook.com
Date Deposited: 24 Jun 2023 07:33
Last Modified: 19 Jun 2024 12:37
URI: http://research.europeanlibrarypress.com/id/eprint/1215

Actions (login required)

View Item
View Item