Multi-Objective Distributed MPC Optimization for UAV Swarm in Low-Altitude Traffic Environments

This study proposes a novel distributed Model Predictive Control (MPC) framework incorporating virtual tube constraints to address the dual challenges of safe navigation and efficient target attainment for Unmanned Aerial Vehicle (UAV) swarms in complex low-altitude traffic environments. An adaptive neighbor interaction mechanism is proposed that dynamically adjusts based on real-time perception and communication capabilities, enabling effective swarm coordination regardless of communication availability. Then, a multi-objective optimization formulation is constructed integrating maximum velocity pursuit, trajectory tracking precision and lateral deviation minimization within virtual tube boundaries. To ensure the stability of terminal velocity, an adaptive speed weight adjustment mechanism was specifically designed. Thus, a computationally efficient distributed MPC is designed utilizing dynamic local state information on switching topology. Through rigorous Lyapunov-based stability analysis and constraint satisfaction proofs, we establish the theoretical guarantees for both asymptotic stability and recursive feasibility of the proposed algorithm. Extensive numerical simulations demonstrate superior performance in complex traffic scenarios, showing faster target convergence while maintaining safe inter-agent distances and lateral deviation margins across various communication-restricted conditions.