Turning Disturbances into Actuation: Hierarchical Environment-Assisted MPC for USV Fault Recovery

Abstract

Thruster failures in unmanned surface vehicles (USVs) can critically compromise mission completion, particularly when severe degradation eliminates controllability in essential degrees of freedom. While traditional fault-tolerant control treats environmental disturbances as impediments to be rejected, this paper presents a novel approach: strategically exploiting wind and wave forces as virtual actuators for emergency harbor return. The proposed environment-assisted model predictive control (EAMPC) framework adaptively modulates environmental force utilization factors based on fault severity and the environmental force prediction confidence, transforming natural disturbances into environmental assistance. The hierarchical architecture integrates state estimation and prediction with physics-informed learning for short-term environmental forces, and reachability-based trajectory planning that exploits environmental forces to expand feasible zones. Theoretical analysis establishes practical input-to-state stability with explicit bounds quantifying degradation. Extensive validation across 320 trials demonstrates 91.25% mission success under 95% thruster degradation compared to 0% for baseline methods. This work demonstrates that strategic environmental exploitation fundamentally transforms fault recovery capabilities in marine robotics.