Nonlinear control of UAVs using multi-layer perceptrons with off-line and on-line learning

Autor:	Kevin Ortega, Subodh Bhandari, Daisy Tang, Amar Raheja, Ohanes Dadian, Ajay Bettadapura
Rok vydání:	2014
Předmět:	Engineering business.product_category Artificial neural network business.industry Inverse Inversion (meteorology) Control engineering Nonlinear control Perceptron Flight simulator Airplane Control theory Feedback linearization business
Zdroj:	ACC
DOI:	10.1109/acc.2014.6859477
Popis:	This paper presents the research on the development of neural network based non-linear controllers for an airplane UAV. Multi-layer perceptrons are used for the training of networks, both off-line and on-line. The data required for off-line training is generated from a validated non-linear flight dynamics model of the Cal Poly Pomona 12' Telemaster UAV. The off-line trained network using multi-layer perceptrons replaces the inverse transformation required for feedback linearization. On-line training is then accomplished to account for the inversion and modeling error. The controllers are tested in the software-in-the-loop simulation environment using FlightGear Flight Simulator. Simulation results compared with flight data are shown. Also shown are the results in the presence of sensor noise.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b31f2726e47de8fcd7edc6f8e67ddf5d https://doi.org/10.1109/acc.2014.6859477 Zobrazit plný text záznamu