Distributed Event-Triggered Optimization for Multiple USVs With Coupled Objective Functions

This article presents an innovative fixed-time optimal control framework for the formation optimization of multiple unmanned surface vehicles (USVs) with coupled objective functions, where each USV's objective function depends not only on its own decision variables but also on those of its neighbors. Unlike conventional algorithms, the proposed framework simultaneously considers the individual states and the dynamic influences of neighbors, making it particularly suitable for realistic systems. The framework guarantees convergence within a fixed time, independent of initial conditions, with a rigorously derived upper bound for the convergence time. To address the challenges of coupling inequality constraints in USV formation optimization, a gradient projection method is used. Furthermore, a communication event-triggered mechanism is introduced to significantly reduce the frequency of communication updates while avoiding Zeno behavior, thereby enhancing resource efficiency. To preserve information privacy and ensure secure operations, auxiliary systems are incorporated into the framework. Additionally, a neural network-based strategy is integrated to handle parameter uncertainties in the dynamic models of USVs, effectively compensating for unknown system variations. Simulation results validate the effectiveness and robustness of the proposed framework in achieving resource-efficient, privacy-preserving, and optimal formation control in multiple USVs systems.