3 citations · 3 across the 5 of their papers we have counts for
9 papers
Demystifying Linear Operator Learning for Control Systems
Max Beier, Nicolas Hoischen, Sandra Hirche +1
This paper proposes a structured approach to learning linear operators for control systems from data. We address both structural and learning-theoretic aspects of the problem. To d…
On the Existence of Quadratic Control Lyapunov Functions for Koopman-Operator based Bilinear Systems
Sami Leon Noel Aziz Hanna, Nicolas Hoischen, Sandra Hirche +1
Koopman operator-based methods enable data-driven bilinear representations of unknown nonlinear control systems. Accurate representations often demand significantly higher dimensio…
Infinitesimal Operator World Models: Finite-Sample Guarantees for Offline Reinforcement Learning
Nicolas Hoischen, Petar Bevanda, Max Beier +3
Continuous-time stochastic processes underlie many natural and engineered systems. In healthcare, autonomous driving, and industrial control, direct interaction with the environmen…
Risk-Aware Trajectory Optimization and Control for an Underwater Suspended Robotic System
Yuki Origane, Nicolas Hoischen, Tzu-Yuan Huang +3
This paper focuses on the trajectory optimization of an underwater suspended robotic system comprising an uncrewed surface vessel (USV) and an uncrewed underwater vehicle (UUV) for…
Toward Near-Globally Optimal Nonlinear Model Predictive Control via Diffusion Models
Tzu-Yuan Huang, Armin Lederer, Nicolas Hoischen +4
Achieving global optimality in nonlinear model predictive control (NMPC) is challenging due to the non-convex nature of the underlying optimization problem. Since commonly employed…
Kernel-Based Optimal Control: An Infinitesimal Generator Approach
Petar Bevanda, Nicolas Hoischen, Tobias Wittmann +3
This paper presents a novel operator-theoretic approach for optimal control of nonlinear stochastic systems within reproducing kernel Hilbert spaces. Our learning framework leverag…
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
Nicolas Hoischen, Petar Bevanda, Stefan Sosnowski +2
This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where b…
Safe Connectivity Maintenance of Underactuated Multi-Agent Networks in Dynamic Oceanic Environments
Nicolas Hoischen, Marius Wiggert, Claire J. Tomlin
Autonomous multi-agent systems are increasingly being deployed in environments where winds and ocean currents have a significant influence. Recent work has developed control polici…
Reconfigurable Plug-and-play Distributed Model Predictive Control for Reference Tracking
Ahmed Aboudonia, Andrea Martinelli, Nicolas Hoischen +1
A plug-and-play model predictive control (PnP MPC) scheme is proposed for varying-topology networks to track piecewise constant references. The proposed scheme allows subsystems to…