Multi-condition event-triggered distributed model predictive control and disturbance compensation for USVs–UAVs formation system

This paper proposes a multi-condition event-triggered distributed model predictive control (DMPC) and disturbance compensation algorithm for unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). The DMPC controller is designed to achieve formation consistency under a leader-follower structure by optimizing the cost function. A nonlinear disturbance observer (NDOB) is integrated to enhance robustness against external disturbances by compensating the predictive model. Additionally, a multi-condition event-triggered mechanism (ETM) is developed to reduce the frequency of online optimization and the associated computational burden by considering multiple triggering conditions. Besides, to achieve precise tracking, this paper also introduces an adaptive weighting mechanism for MPC. Simulation results demonstrated that the proposed algorithm outperforms traditional MPC. The algorithm maintains effective path following and formation keeping even with the event-triggered mechanism, offering a novel technical approach for the efficient mission execution of USVs–UAVs formation systems.