Zobrazeno 1 - 10
of 1 482
pro vyhledávání: '"P. Berberich"'
This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data c
Externí odkaz:
http://arxiv.org/abs/2411.05624
In this paper, we propose a novel controller design approach for unknown nonlinear systems using the Koopman operator. In particular, we use the recently proposed stability- and certificate-oriented extended dynamic mode decomposition (SafEDMD) archi
Externí odkaz:
http://arxiv.org/abs/2411.03875
Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garner
Externí odkaz:
http://arxiv.org/abs/2410.21117
We investigate nonlinear model predictive control (MPC) with terminal conditions in the Koopman framework using extended dynamic mode decomposition (EDMD) to generate a data-based surrogate model for prediction and optimization. We rigorously show re
Externí odkaz:
http://arxiv.org/abs/2408.12457
Autor:
Funcke, Niklas, Berberich, Julian
Noise in quantum computing devices poses a key challenge in their realization. In this paper, we study the robustness of optimal quantum annealing protocols against coherent control errors, which are multiplicative Hamlitonian errors causing detrimen
Externí odkaz:
http://arxiv.org/abs/2408.06782
Autor:
Berberich, Julian, Allgöwer, Frank
The development of control methods based on data has seen a surge of interest in recent years. When applying data-driven controllers in real-world applications, providing theoretical guarantees for the closed-loop system is of crucial importance to e
Externí odkaz:
http://arxiv.org/abs/2406.04130
This paper presents an adaptive tracking model predictive control (MPC) scheme to control unknown nonlinear systems based on an adaptively estimated linear model. The model is determined based on linear system identification using a moving window of
Externí odkaz:
http://arxiv.org/abs/2405.09852
Data-driven controllers design is an important research problem, in particular when data is corrupted by the noise. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme using noisy input-state data for unknown linear
Externí odkaz:
http://arxiv.org/abs/2404.19096
Autor:
Schneider, Jan, Berberich, Julian
Quantum computing provides a powerful framework for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in systems and control theory. In th
Externí odkaz:
http://arxiv.org/abs/2403.17711
The Koopman operator serves as the theoretical backbone for machine learning of dynamical control systems, where the operator is heuristically approximated by extended dynamic mode decomposition (EDMD). In this paper, we propose Stability- and certif
Externí odkaz:
http://arxiv.org/abs/2402.03145