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A coordinated control challenge is addressed in 6-degree-of-freedom (6-DOF) fixed-wing multiple autonomous aerial vehicle (multi-AAV) systems under communication and state constraints. The primary obstacle in achieving this goal arises from managing frequent information interactions and the assurance that UAV states converge within prescribed bounds. On the one hand, a novel dynamic self-triggering mechanism is effectively proposed. Unlike current state-of-the-art approaches, the proposed dynamic self-triggering communication mechanism features a larger triggering threshold and eliminates the need for continuous monitoring of system state information. This reduces the demand on system communication and sensor resources. On the other hand, a new time-varying constraint bounded function is introduced to effectively relax restrictions on the initial system state. Then, the coordinated translational/rotational controllers are designed to ensure minimal consensus tracking error. Semi-physical simulations highlight the effectiveness of the proposed control algorithm. Note to Practitioners—In actual environment, the multi-UAVs flight always requires inter-communication to ensure the stable performance of the entire formation. However, period-based communication leads to a waste of communication resources. The event-triggering communication mechanism lowers the communication frequency of UAVs, thereby reducing energy consumption. Nevertheless, most existing control results on event-triggered communication overlook the fact that continuous monitoring of state information still causes unnecessary energy consumption. To further investigate the problem, a dynamic self-triggering mechanism is proposed in this study, which can determine the subsequent triggered moment based on the state information of the current triggered moment. In addition, the state of UAVs due to safety and physical constraints ought to be constrained. Therefore, a prescribed-time constrained control strategy is proposed, which not only improves the transient performance (e.g. small overshoot and fast adjustment time), but also ensures that the UAV state converges within a given constraint bound.
Shi et al. (Wed,) studied this question.