Fusion of Indirect Methods and Iterative Learning for Persistent Velocity Trajectory Optimization of a Sustainably Powered Autonomous Surface Vessel
The research developed a real-time velocity optimization method for solar-powered autonomous surface vessels that avoids needing a solar forecast. It combines indirect optimal control with iterative learning to create a closed-form control law. This approach yields performance comparable to complex model predictive control, proving it is computationally efficient and robust against weather disturbances.