arXiv subjects

Lauritz Rismark Fosso

Publications and source records attributed to Lauritz Rismark Fosso.

3 recordsLinked to original sources

arXiv2026

Sim2Swim: Zero-Shot Velocity Control for Agile AUV Maneuvering in 3 Minutes

Holonomic autonomous underwater vehicles (AUVs) have the hardware ability for agile maneuvering in both translational and rotational degrees of freedom (DOFs). However, due to challenges inherent to underwater vehicles, such as complex hydrostatics and hydrodynamics, parametric uncertainties, and frequent changes in dynamics due to payload changes, control is challenging. Performance typically relies on carefully tuned controllers targeting unique platform configurations, and a need for re-tuning for deployment under varying payloads and hydrodynamic conditions. As a consequence, agile maneuvering with simultaneous tracking of time-varying references in both translational and rotational DOFs is rarely utilized in practice. To the best of our knowledge, this paper presents the first general zero-shot sim2real deep reinforcement learning-based (DRL) velocity controller enabling path following and agile 6DOF maneuvering with a training duration of just 3 minutes. Sim2Swim, the proposed approach, inspired by state-of-the-art DRL-based position control, leverages domain randomization and massively parallelized training to converge to field-deployable control policies for AUVs of variable characteristics without post-processing or tuning. Sim2Swim is extensively validated in pool trials for a variety of configurations, showcasing robust control for highly agile motions.

cs.RO↗

arXiv2026

KalMRACO: Unifying Kalman Filtering and Model Reference Adaptive Control for Robust Control and Estimation

A common assumption when applying the Kalman filter is a priori knowledge of the system parameters. These parameters are not necessarily known, and this may limit the real-world applicability of the Kalman filter. The well-established Model Reference Adaptive Controller (MRAC) utilizes a known reference model and ensures that the input-output behavior of a potentially unknown system converges to that of the reference model. We present KalMRACO, a unification of Kalman filtering and MRAC leveraging the reference model of MRAC as the Kalman filter system model, thus eliminating, to a large degree, the need for knowledge of the underlying system parameters in the application of the Kalman filter. We also introduce the concept of blending estimated states and measurements in the feedback law to ensure stability during the initial transient. KalMRACO is validated through simulations and lab trials on an underwater vehicle. Results show superior tracking of the reference model state, observer state convergence, and noise mitigation properties.

eess.SY↗

arXiv2026

Tracking the Effective Surface Area of Non-Convex Satellites

This paper presents a novel framework to track the effective surface area of non-convex satellites, enabling the use of aerodynamic drag in low Earth orbit for orbital control. The proposed framework enables the satellite to track the effective surface area while simultaneously performing other maneuvers. We introduce this framework through a backstepping control algorithm, and exemplify its advantages with an extension, to simultaneously maximize solar panel exposure. The equilibria of the closed-loop systems are shown to be asymptotically stable, and simulation results confirm the effectiveness of the proposed framework.

eess.SY↗