Hannes Homburger

University of Freiburg & HTWG Konstanz

Thursday, June 18, 2026, 13:00

SR 02-016/18, Geb. 101

The efficient control of surface vessels in strong, changing environmental conditions is a challenging problem in marine engineering. This thesis presents theoretical investigations, methodological developments, and practical applications of numerical optimal control for surface vessel applications, with a focus on energy-efficient operation, even in strong currents.

The theoretical contribution of this thesis focuses on Model Predictive Path Integral (MPPI) control, a sampling-based method gaining attention in the robotics community. The inherent suboptimality of MPPI control is analyzed and quantified. This theoretical insight provides the foundation for the deterministic MPPI method, which is introduced and applied to solve nonconvex and nonsmooth optimal control problems. To address the demands of rapidly changing environments, a skill-based extension to MPPI is presented. To improve scalability and convergence, insights from Newton-type optimization are incorporated into the MPPI framework, resulting in the Gauss-Newton accelerated MPPI approach.

Optimal control methods are applied to surface vessel control through the design of different model predictive control approaches. By leveraging estimation and learning techniques, these controllers achieve high performance while maintaining real-time capability on embedded hardware. The optimal control strategies are investigated on a Hardware-in-the-Loop (HIL) system and on the research vessel Solgenia operating on the Rhine River, demonstrating high performance and robustness to severe currents in real-world conditions.

Finally, the flexibility and practical relevance of this framework are demonstrated by integrating the optimal control formulation into a decision support system for the commercial passenger ferry MS Insel Mainau to improve energy-efficient operation.