Robust Adaptive Formation Control of USVs with the Event-Triggered Mechanism

Abstract

This note focuses on the application of the event-triggered mechanism into the formation control system. For this purpose, a novel fleet control model is established in the Cartesian coordinate system. Through this structure, a model-based event-triggered control (ETC) is designed by utilizing the radial basic function neural networks (RBF NNs) and the minimum learning parameter (MLP) technique. Thus, the continuous acquisition of the formation state does not take longer, and the communication load of the resource-limited fleet is largely reduced. In addition, the semi-global uniformly ultimately bounded (SGUUB) of all signals are proved by the Lyapunov candidate function. And the corresponding simulation results can be used to verify the effectiveness and robustness of the proposed control scheme.