Iterative Learning-Based Nonlinear Model Predictive Control of an Underactuated Autonomous Surface Vessel in Current Fields

Hannes Homburger, Katrin Baumgärtner, Stefan Wirtensohn, Moritz Diehl, Johannes Reuter

Abstract: Efficient and safe autonomous control of surface vessels is seminal for the future of maritime transport systems. In this paper, we use an iterative learning–based nonlinear model predictive control scheme leveraging past experiences of the motion of vessels in a current field to reach optimal behavior. We define an optimal control problem including a detailed vessel model but only a roughly estimated current model. This current model is improved from trial to trial. The learned controller is compared to a linear track controller, a zero–offset nonlinear model predictive controller without current information, and a nonlinear model predictive controller including a perfect model of the current field. The results of this comparison show that by including experiences from previous trials, the controller can improve its performance significantly.We believe that numerical optimal control has the potential to disrupt the future control design of maritime systems.