Distributed Rotating Encirclement Control of Strict-Feedback Multi-Agent Systems Using Bearing Measurements

This paper investigates the distributed multi-target rotating encirclement formation problem of strict-feedback multi-agent systems using the targets' bearing angles and the agents' known positions, where all agents are forced to achieve even circular formation around the targets' geometric center. Firstly, an estimator is proposed for each agent to localize the neighbor targets. Secondly, based on the trajectory planning method, a reference trajectory is constructed by three estimators, which are used to obtain the targets' geometric center, the reference rotating radius and angular. Then, the proposed adaptive neural dynamic surface control law forces each agent to move along the reference trajectory, which satisfies the multi-target rotating encirclement formation conditions.