Event-Triggered Fully Distributed Control: A Model-Free Adaptive Learning Algorithm

This article studies the consensus problem in multiagent systems under the challenge of an unknown system model and limited communication resources. A novel model-free adaptive learning algorithm is developed to learn the controller from system data. A model-based event-triggered fully distributed control (ET-FDC) algorithm is proposed to achieve consensus while saving the limited communication resources. Furthermore, a data-driven systematic learning methodology for the ET-FDC algorithm is introduced to eliminate the need for a system model. Compared with existing approaches, the main advantages of the proposed method are that it not only avoids using the system model and global topology information, but also reduces unnecessary communication. Finally, simulations are presented to illustrate the superiority of the theoretical results.