230 citations · 372 across the 23 of their papers we have counts for
22 papers · 1 filter
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…
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 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…
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…
Stability and performance in MPC using a finite-tail cost
Johannes Köhler, Frank Allgöwer
In this paper, we provide a stability and performance analysis of model predictive control (MPC) schemes based on finite-tail costs. We study the MPC formulation originally propose…
A nonlinear tracking model predictive control scheme for dynamic target signals
Johannes Köhler, Matthias A. Müller, Frank Allgöwer
We present a nonlinear model predictive control (MPC) scheme for tracking of dynamic target signals. The scheme combines stabilization and dynamic trajectory planning in one layer,…