Energy-Efficient Model Predictive Control for Electric Inland Surface Vessels

Abstract

Energy-efficient trajectory tracking for surface vessels commonly relies on propulsion power models that neglect inflow, and therefore changes in the propeller operating point. For vessels operating near a single nominal condition this is a reasonable simplification, but for overactuated inland vessels with azimuth thrusters that turn, stop, and accelerate frequently, the operating point shifts continually and the unmodeled power variations translate into avoidable energy use. On electric vessels, this dependence is inexpensive to recover: thruster power is directly measurable at the inverter DC side, without dedicated instrumentation. We exploit this to experimentally identify an inflow-aware thruster power model and use it as the economic stage cost of a nonlinear model predictive control (NMPC) trajectory tracking controller with embedded thrust allocation and actuator constraints. We evaluate the approach on an electric inland test vessel, benchmarking it against an otherwise identical NMPC controller with a conventional cubic shaft-speed model as its stage cost. In simulation, the inflow-aware formulation yields only a small benefit of 2.13 percent in near-nominal sailing but reduces energy consumption by 11.3 percent on a maneuver-intensive ferry route. Further field experiments show an average energy reduction of 13.4 percent, consistent with the simulated result.