An improved flocking control algorithm to solve the effect of individual communication barriers on flocking cohesion in multi-agent systems

Flocking cohesion is a crucial factor for groups to maintain aggregation. Communication barriers can significantly challenge the maintenance of flocking aggregation and integrity. Although several studies have focused on communication barriers between agents and the Leader, less attention has been given to agents' self-communication barriers. This paper investigates the effect of two kinds of communication barriers that coexist on flocking cohesion. One is the communication barrier between agents and the Leader. There are informed and uninformed agents, with informed agents receiving different degrees of complete information about the Leader. The other is the agent's self-communication barrier, where each agent has a failure rate that prevents it from obtaining and transmitting information. Then, a novel potential function is designed by analyzing the correlation between the potential function and flocking cohesion. It enhances flocking cohesion and reduces flocking time. Moreover, the flocking control algorithm is improved by incorporating a local feedback mechanism. It allows informed agents to transfer information more broadly and promotes interaction among agents, further enhancing flocking cohesion and integrity. Stability analysis is performed using the Lyapunov theorem. Finally, we propose three criteria for evaluating flocking performance. Based on these criteria, simulation results demonstrate the proposed potential function and control algorithm's significant advantages. This control algorithm is applicable for addressing individual communication issues in swarming drones or flocking robots during missions in engineering applications.