Journal article
Distributed Artificial Neural Networks-Based Adaptive Strictly Negative Imaginary Formation Controllers for Unmanned Aerial Vehicles in Time-Varying Environments
IEEE transactions on industrial informatics, Vol.17(6), pp.3910-3919
06/2021
Metrics
10 Record Views
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Abstract
Formation control techniques have been widely implemented in networked multirobot systems. In this article, we present a novel framework for swarm multiagent systems based on the relative-position output feedback consensus supported with the new concept of adaptive strictly negative imaginary consensus controllers, leveraging the learning capability of artificial neural networks. For experimental validation, we consider the case of two quadcopters moving together while carrying a dynamic load. We employ Kharitonov's theorem to study the stability of the proposed adaptive control systems. Finally, a rigorous real-time experimental study is conducted to highlight the merits of the proposed formation control algorithms.
Details
- Title
- Distributed Artificial Neural Networks-Based Adaptive Strictly Negative Imaginary Formation Controllers for Unmanned Aerial Vehicles in Time-Varying Environments
- Creators
- Vu Phi Tran - University of New South WalesFendy Santoso - University of New South WalesMatthew A. Garratt - University of New South WalesSreenatha G. Anavatti - University of New South Wales
- Publication Details
- IEEE transactions on industrial informatics, Vol.17(6), pp.3910-3919
- Publisher
- IEEE
- Grant note
- University of New South Wales Canberra; UNSW Canberra (10.13039/100012481)
- Identifiers
- 991013092672802368
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article