Prescribed-Time Formation Tracking Control With Collision Avoidance Under Limited Information

Shun Yang, Qiang Xiao, Zhigang Zeng

Abstract: In this paper, we address the problem of prescribed-time formation tracking under collision avoidance for a first-order multi-intelligent system with a directed spanning tree communication topology. By introducing a time-varying function, a new null-space-based of the prescribed-time behavioral control algorithm is proposed for driving all followers to approach the dynamic leader within a prescribed time and guaranteeing no collision among followers. The algorithm can be pre-specified with parameters designed for the user and is independent of the initial conditions or controller parameters. In particular, the velocities of the newly designed behaviors, as an important part of the algorithm, complete the tasks within the prescribed time in a distributed manner respectively. We design a new Lyapunov candidate function and analyze the prescribed-time convergence of the closed-loop system under behavioral conflicts. Finally, the effectiveness of the algorithm is verified by simulation examples with four agents.