Disturbance-Adaptive Differentiable MPC for Underwater Structure Inspection Using Underwater Robot

Abstract

Close-proximity inspection of underwater cylindrical structures is challenging due to nonlinear vehicle dynamics, flow disturbances, and payload uncertainty. Fixed-weight MPC provides structured constraint handling but lacks adaptivity, while model-free RL is adaptive but often unstable and unsafe under disturbances. We propose Marine AC-MPC, which combines a differentiable iLQR-based MPC layer with an actor-critic framework that learns time-varying MPC cost weights online. In MarineGym, the proposed method achieves more reliable orbit tracking and higher success rates than fixed-weight MPC and PPO baselines under disturbed conditions.