808 citations · 2.6k across the 51 of their papers we have counts for
58 papers
Koopman meets input-output data: Data-driven output-feedback control of nonlinear systems with closed-loop guarantees
Robin Strässer, Julian Berberich, Manuel Schaller +2
Data-driven control of nonlinear systems from input-output measurements remains a fundamental challenge, as existing approaches with rigorous closed-loop guarantees predominantly r…
A Koopman Set-Membership Approach for Nonlinear Data-Driven Control with Stability Guarantees
Yifan Xie, Zuxun Xiong, Julian Berberich +2
This paper proposes a data-driven controller design method for unknown nonlinear systems based on a Koopman bilinear realization. Using Koopman operator theory, the nonlinear syste…
Data-Driven Min-Max MPC with Integral Quadratic Constraints
Yifan Xie, Julian Berberich, Frank Allgöwer
Data-driven control of nonlinear systems with rigorous guarantees is a challenging control problem. Integral quadratic constraints (IQCs) provide a powerful framework for modeling…
Adaptive Data-Driven Min-Max MPC for Linear Time-Varying Systems
Yifan Xie, Julian Berberich, Frank Allgöwer
In this paper, we propose an adaptive data-driven min-max model predictive control (MPC) scheme for discrete-time linear time-varying (LTV) systems. We assume that prior knowledge…
An overview of Koopman-based control: From error bounds to closed-loop guarantees
Robin Strässer, Karl Worthmann, Igor Mezić +3
Controlling nonlinear dynamical systems remains a central challenge in a wide range of applications, particularly when accurate first-principle models are unavailable. Data-driven…
Bilinear Data-Driven Min-Max MPC: Designing Rational Controllers via Sum-of-squares Optimization
Yifan Xie, Julian Berberich, Robin Strässer +1
We propose a data-driven min-max model predictive control (MPC) scheme to control unknown discrete-time bilinear systems. Based on a sequence of noisy input-state data, we state a…
Kernel-based error bounds of bilinear Koopman surrogate models for nonlinear data-driven control
Robin Strässer, Manuel Schaller, Julian Berberich +2
We derive novel deterministic bounds on the approximation error of data-based bilinear surrogate models for unknown nonlinear systems. The surrogate models are constructed using ke…
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…
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…
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…
An overview of systems-theoretic guarantees in data-driven model predictive control
Julian Berberich, Frank Allgöwer
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 theoretic…
Adaptive tracking MPC for nonlinear systems via online linear system identification
Tatiana Strelnikova, Johannes Köhler, Julian Berberich
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 dete…
Data-Driven Min-Max MPC for Linear Systems: Robustness and Adaptation
Yifan Xie, Julian Berberich, Frank Allgöwer
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…
SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems
Robin Strässer, Manuel Schaller, Karl Worthmann +2
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…
Koopman-based feedback design with stability guarantees
Robin Strässer, Manuel Schaller, Karl Worthmann +2
We present a method to design a state-feedback controller ensuring exponential stability for nonlinear systems using only measurement data. Our approach relies on Koopman-operator…
A Linear Parameter-Varying Approach to Data Predictive Control
Chris Verhoek, Julian Berberich, Sofie Haesaert +2
By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-fee…
Data-Driven Min-Max MPC for Linear Systems
Yifan Xie, Julian Berberich, Frank Allgower
Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are…
Robust data-driven control for nonlinear systems using the Koopman operator
Robin Strässer, Julian Berberich, Frank Allgöwer
Data-driven analysis and control of dynamical systems have gained a lot of interest in recent years. While the class of linear systems is well studied, theoretical results for nonl…
Control of bilinear systems using gain-scheduling: Stability and performance guarantees
Robin Strässer, Julian Berberich, Frank Allgöwer
In this paper, we present a state-feedback controller design method for bilinear systems. To this end, we write the bilinear system as a linear fractional representation by interpr…
Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems
Chris Verhoek, Julian Berberich, Sofie Haesaert +2
We derive direct data-driven dissipativity analysis methods for Linear Parameter-Varying (LPV) systems using a single sequence of input-scheduling-output data. By means of construc…
Sequential learning and control: Targeted exploration for robust performance
Janani Venkatasubramanian, Johannes Köhler, Julian Berberich +1
We present a novel dual control strategy for uncertain linear systems based on targeted harmonic exploration and gain-scheduling with performance and excitation guarantees. In the…
Linear Data-Driven Economic MPC with Generalized Terminal Constraint
Yifan Xie, Julian Berberich, Frank Allgöwer
In this paper, we propose a data-driven economic model predictive control (EMPC) scheme with generalized terminal constraint to control an unknown linear time-invariant system. Our…
Data-driven Nonlinear Predictive Control for Feedback Linearizable Systems
Mohammad Alsalti, Victor G. Lopez, Julian Berberich +2
We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a no…