Formation Reconfiguration for Underwater Gliders Under Ocean Current Disturbances: An Enhanced Distributed Model Predictive Control Algorithm

Underwater glider (UG) formations exhibit significant trajectory deviations when subjected to non-uniform ocean currents, complicating spatial reconfiguration upon exiting complex marine environments and compromising environmental monitoring fidelity. To solve these problems, we develop a rapid formation reconfiguration algorithm based on distributed model predictive control (DMPC). The algorithm combines a flexible boundary-triggering mechanism for autonomous control suspension and rapid activation, a nonlinear vector heading pre-compensation scheme to counteract strong current disturbances, and a multi-objective leader capability assessment with a dynamic rotation strategy for optimized leader selection. The predictive plant model is calibrated utilizing empirical sea trial datasets, and its control performance is validated through comparative simulations against conventional MPC and active disturbance rejection control (ADRC). The results demonstrate that the proposed algorithm reduces the standard deviation of lateral inter-glider distance by 42% and 17% relative to ADRC and conventional MPC, shortens the reconfiguration time to 13.3 h, and lowers the maximum relative deflection, lateral distance standard deviation and average energy consumption. This framework significantly improves formation stability, reconfiguration efficiency, and energy efficiency, providing a robust methodology for persistent swarm deployments.