Event-Triggered Train Formation Control of Multiple Autonomous Surface Vehicles in Polar Communication Interference Environment

This paper investigates the event-triggered train formation control problem for multiple autonomous surface vehicles (ASVs) formation system in polar communication interference environment. Firstly, a distributed resilient guidance algorithm is introduced to generate the reference route based on waypoints. In the guidance algorithm, the distributed resilient leader predictor (RLP) is applied to obtain the states of ice-breaking ship when communication fails, and the resilient train formation scheme is designed to compute the reference signals for ASVs. Subsequently, an adaptive neural event-triggered train formation control algorithm is developed. In the control algorithm, the neural networks (NNs) are conducted to approximate model uncertainties, and event-triggered control (ETC) is employed to minimize controller updates. Furthermore, the threshold of the event-triggered mechanism (ETM) can be dynamically adjusted by states of system. It is proved that the formulated algorithm can ensure the prediction errors converge and multiple ASVs system is stable in polar communication interference environment. Finally, two simulation experiments are adopted to illustrate the effectiveness of the proposed algorithm.