Distributed Artificial Neural Networks-Based Adaptive Strictly Negative Imaginary Formation Controllers for Unmanned Aerial Vehicles in Time-Varying Environments

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.