Zobrazeno 1 - 10
of 897
pro vyhledávání: '"Allgoewer, A."'
In this paper, we provide a tutorial overview and an extension of a recently developed framework for data-driven control of unknown nonlinear systems with rigorous closed-loop guarantees. The proposed approach relies on the Koopman operator represent
Externí odkaz:
http://arxiv.org/abs/2411.10359
Many cyber-physical systems can naturally be formulated as switched systems with constrained switching. This includes systems where one of the signals in the feedback loop may be lost. Possible sources for losses are shared or unreliable communicatio
Externí odkaz:
http://arxiv.org/abs/2411.08436
Autor:
Brändle, Felix, Allgöwer, Frank
In this paper, we present a new parametrization to perform direct data-driven analysis and controller synthesis for the error-in-variables case. To achieve this, we employ the Sherman-Morrison-Woodbury formula to transform the problem into a linear f
Externí odkaz:
http://arxiv.org/abs/2411.06787
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
We propose a novel layer-wise parameterization for convolutional neural networks (CNNs) that includes built-in robustness guarantees by enforcing a prescribed Lipschitz bound. Each layer in our parameterization is designed to satisfy a linear matrix
Externí odkaz:
http://arxiv.org/abs/2410.22258
Autor:
Brändle, Felix, Allgöwer, Frank
This paper introduces a novel parameterization to characterize unknown linear time-invariant systems using noisy data. The presented parameterization describes exactly the set of all systems consistent with the available data. We then derive verifiab
Externí odkaz:
http://arxiv.org/abs/2410.10681
In this paper we propose an end-to-end algorithm for indirect data-driven control for bilinear systems with stability guarantees. We consider the case where the collected i.i.d. data is affected by probabilistic noise with possibly unbounded support
Externí odkaz:
http://arxiv.org/abs/2409.18010
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:
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