Distributed optimal cooperative control of unmanned surface vehicles based on hybrid dynamic event-triggered mechanism

This paper investigates the problem of distributed optimal cooperative control (DOCC) for unmanned surface vehicles (USVs) under communication resource constraints. Distributed cooperative control algorithms that disregard channel resource limitations may result in severe navigation accidents. Consequently, we propose a DOCC algorithm that conserves communication resources. The algorithm under consideration adopts a two-layer structure, comprising a co-optimization layer and a trajectory tracking layer. The optimization layer determines the optimal position of each USV utilizing a well-designed auxiliary system. Concurrently, a hybrid dynamic event-triggered mechanism (HDETM) has been developed in the auxiliary system. This method features an adjustable minimum inter-event time (MIET), offering greater flexibility compared to the presented schemes and effectively addressing the issue of limited channel resources. The tracking layer employs a neural network-based backstepping method to achieve target tracking. In comparison with the existing methods, this algorithm significantly reduces the frequency of information exchange without loss of control accuracy. Finally, the effectiveness and superiority of this method were validated through numerical simulations of USVs. The results demonstrate that HDETM significantly reduces communication resource consumption, achieving a 98% reduction compared to the continuous event-triggered mechanism and at least a 46% reduction compared to the periodic event-triggered mechanism.