230 citations · 372 across the 23 of their papers we have counts for
27 papers
Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning
Yuzhou Wei, Wenjie Liu, Yifan Xie +3
Data-driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing da…
On stability and non-averaged performance of economic MPC with terminal conditions for optimal periodic operation
Jonas Mair, Lukas Schwenkel, Matthias A. Müller +1
Operation at steady state is often not optimal when optimizing over an economic cost objective. In many cases, periodic operation yields better performance. Therefore, we derive as…
Discrete-Time Event-Triggered Extremum Seeking
Victor Hugo Pereira Rodrigues, Tiago Roux Oliveira, Miroslav Krstić +1
This paper proposes a discrete-time event-triggered extremum seeking control scheme for real-time optimization of nonlinear systems. Unlike conventional discrete-time implementatio…
Robust IMMPC: An Offset-free MPC for Rejecting Unknown Disturbances
Felix Brändle, Frank Allgöwer
Output regulation is the problem of finding a control input to asymptotically track reference trajectories and reject disturbances. This can be addressed by using the internal mode…
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…
Safety for Weakly-Hard Control Systems via Graph-Based Barrier Functions
Marc Seidel, Mahathi Anand, Frank Allgöwer
Despite significant advancement in technology, communication and computational failures are still prevalent in safety-critical engineering applications. Often, networked control sy…
Comparison and performance analysis of dynamic encrypted control approaches
Sebastian Schlor, Frank Allgöwer
Encrypted controllers using homomorphic encryption have proven to guarantee the privacy of measurement and control signals, as well as system and controller parameters, while regul…
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…
Output-feedback model predictive control under dynamic uncertainties using integral quadratic constraints
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +1
In this work, we propose an output-feedback tube-based model predictive control (MPC) scheme for linear systems under dynamic uncertainties that are described via integral quadrati…
On the Completeness and Ordering of Path-Complete Barrier Functions
Mahathi Anand, Raphaël Jungers, Majid Zamani +1
This paper is concerned with path-complete barrier functions which offer a graph-based methodology for verifying safety properties in switched systems. The path-complete framework…
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…
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…
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…
Robust peak-to-peak gain analysis using integral quadratic constraints
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +1
This work provides a framework to compute an upper bound on the robust peak-to-peak gain of discrete-time uncertain linear systems using integral quadratic constraints (IQCs). Such…