Event-Triggered Multiple Dynamic Targets Formation Tracking Without Well-Informed Agent: A General Exploring Relationship

In this paper, the multiple dynamic targets formation tracking (MDTFT) problem is studied for multi-agent systems. The objective is to drive locally connected agents to form the predefined time-varying formation meanwhile tracking the convex hull spanned by multiple targets. In existing results, agents are divided into well-informed and uninformed ones, where it is assumed that the well-informed agents must obtain the information of all the targets. However, in large-scale deployment scenarios, exploring all the targets is a daunting task for well-informed agents. To handle this problem, a new framework is designed to solve the MDTFT problems without well-informed agent. Each agent is allowed to explore any number of targets depending on its own capability, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general exploring relationship</i> . Then, by using the adaptive mechanism and boundary layer technique, a fully distributed MDTFT algorithm with adaptive gains is constructed to avoid the global information and control chattering. Further, a stochastic event-triggered MDTFT algorithm is specifically conceived, in which the continuous communication among agents can be avoided. Compared with the existing event-triggered schemes, the stochastic event-triggered scheme can significantly reduce the triggering times. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed MDTFT algorithms.