230 citations · 372 across the 23 of their papers we have counts for
10 papers · 1 filter
Robust targeted exploration for systems with non-stochastic disturbances
Janani Venkatasubramanian, Johannes Köhler, Mark Cannon +1
We propose a novel targeted exploration strategy designed specifically for uncertain linear time-invariant systems with energy-bounded disturbances, i.e., without any assumptions o…
Robust performance for switched systems with constrained switching and its application to weakly hard real-time control systems
Simon Lang, Marc Seidel, Frank Allgöwer
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 l…
Data-Driven Min-Max MPC for LPV Systems with Unknown Scheduling Signal
Yifan Xie, Julian Berberich, Felix Brändle +1
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,…
Koopman-based control of nonlinear systems with closed-loop guarantees
Robin Strässer, Julian Berberich, Manuel Schaller +2
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 guar…
A System Parametrization for Direct Data-Driven Analysis and Control with Error-in-Variables
Felix Brändle, Frank Allgöwer
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 Sher…
Koopman-based control using sum-of-squares optimization: Improved stability guarantees and data efficiency
Robin Strässer, Julian Berberich, Frank Allgöwer
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 fe…
LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
Patricia Pauli, Ruigang Wang, Ian Manchester +1
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…
A System Parameterization for Direct Data-Driven Estimator Synthesis
Felix Brändle, Frank Allgöwer
This paper introduces a novel parameterization to characterize unknown linear time-invariant systems using noisy data. The presented parameterization describes exactly the set of a…
End-to-end guarantees for indirect data-driven control of bilinear systems with finite stochastic data
Nicolas Chatzikiriakos, Robin Strässer, Frank Allgöwer +1
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. da…
Data-driven MPC with terminal conditions in the Koopman framework
Karl Worthmann, Robin Strässer, Manuel Schaller +2
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…