Robust Dual-Loop MPC for Variable-Mass Feeding UAVs with Lyapunov Small-Gain Guarantees

Feeding unmanned aerial vehicles (UAVs) in aquaculture face critical challenges due to time-varying mass, strong coupling, and environmental disturbances, which hinder the effectiveness of conventional control strategies. This paper proposes a robust dual-loop model predictive control (MPC) framework optimized by an adaptive niche radius genetic algorithm (ANRGA). The outer loop employs MPC for position regulation using virtual acceleration inputs, while the inner loop applies MPC for attitude stabilization with dynamic inertia adaptation. To overcome the limitations of manual weight tuning, ANRGA adaptively optimizes the weighting factors, preventing premature convergence and improving global search capability. System stability is theoretically ensured through Lyapunov analysis and the small-gain theorem, even under variable-mass dynamics. MATLAB simulations under representative trajectories—including spiral, figure-eight, and feeding cruise paths—demonstrate that the proposed ANRGA-MPC-MPC achieves position errors below 0.5 m, enhances response speed by approximately 58% compared with conventional MPC, and outperforms benchmark controllers in terms of accuracy, robustness, and convergence. These results confirm the feasibility of the proposed method for precise and energy-efficient UAV feeding operations, providing a promising control strategy for intelligent aquaculture applications.