187 citations · 603 across the 24 of their papers we have counts for
25 papers
A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
Angela P. Schoellig, Matthias A. Müller, Sebastian Trimpe +8
Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection…
Gaussian Process-Based Nonlinear Moving Horizon Estimation
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. On the one hand, we approximate the system dynamic…
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems
Mohammad Alsalti, Victor G. Lopez, Matthias A. Müller
In this paper, we present a Q-learning algorithm to solve the optimal output regulation problem for discrete-time LTI systems. This off-policy algorithm only relies on using persis…
Notes on data-driven output-feedback control of linear MIMO systems
Mohammad Alsalti, Victor G. Lopez, Matthias A. Müller
Recent works have approached the data-driven design of dynamic output-feedback controllers for discrete-time LTI systems by constructing non-minimal state vectors composed of past…
Model predictive control for the prescription of antithyroid agents
Maylin Menzel, Tobias M. Wolff, Johannes W. Dietrich +1
Although hyperthyroidism is a common disease, the pharmaceutical therapy is based on a trial-and-error approach. We extend a mathematical model of the pituitary-thyroid feedback lo…
On an integral variant of incremental input/output-to-state stability and its use as a notion of nonlinear detectability
Julian D. Schiller, Matthias A. Müller
We propose a time-discounted integral variant of incremental input/output-to-state stability (i-iIOSS) together with an equivalent Lyapunov function characterization. Continuity of…
Robust Stability of Gaussian Process Based Moving Horizon Estimation
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
In this paper, we introduce a Gaussian process based moving horizon estimation (MHE) framework. The scheme is based on offline collected data and offline hyperparameter optimizatio…
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…
Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version
Matthias Köhler, Matthias A. Müller, Frank Allgöwer
We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents s…
Robust Data-Driven Moving Horizon Estimation for Linear Discrete-Time Systems
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced. The scheme solely relies on offline collec…
Stability in data-driven MPC: an inherent robustness perspective
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
Data-driven model predictive control (DD-MPC) based on Willems' Fundamental Lemma has received much attention in recent years, allowing to control systems directly based on an impl…
Online convex optimization for data-driven control of dynamical systems
Marko Nonhoff, Matthias A. Müller
We propose an algorithm based on online convex optimization for controlling discrete-time linear dynamical systems. The algorithm is data-driven, i.e., does not require a model of…
Practical exponential stability of a robust data-driven nonlinear predictive control scheme
Mohammad Alsalti, Victor G. Lopez, Julian Berberich +2
We provide theoretical guarantees for recursive feasibility and practical exponential stability of the closed-loop system of a feedback linearizable nonlinear system when controlle…
Data-based Control of Feedback Linearizable Systems
Mohammad Alsalti, Victor G. Lopez, Julian Berberich +2
We present an extension of Willems' Fundamental Lemma to the class of multi-input multi-output discrete-time feedback linearizable nonlinear systems, thus providing a data-based re…
A simple suboptimal moving horizon estimation scheme with guaranteed robust stability
Julian D. Schiller, Boyang Wu, Matthias A. Müller
We propose a suboptimal moving horizon estimation (MHE) scheme for a general class of nonlinear systems. To this end, we consider an MHE formulation that optimizes over the traject…
Data-driven distributed MPC of dynamically coupled linear systems
Matthias Köhler, Julian Berberich, Matthias A. Müller +1
In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to dec…
Data-Based Moving Horizon Estimation for Linear Discrete-Time Systems
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
This paper introduces a data-based moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems. The scheme solely relies on collected data without employ…
Suboptimal nonlinear moving horizon estimation
Julian D. Schiller, Matthias A. Müller
In this paper, we propose a suboptimal moving horizon estimator for a general class of nonlinear systems. For the stability analysis, we transfer the "feasibility-implies-stability…
Data-driven model predictive control: closed-loop guarantees and experimental results
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We provide a comprehensive review and practical implementation of a recently developed model predictive control (MPC) framework for controlling unknown systems using only measured…
Linear tracking MPC for nonlinear systems Part II: The data-driven case
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We present a novel data-driven model predictive control (MPC) approach to control unknown nonlinear systems using only measured input-output data with closed-loop stability guarant…
Linear tracking MPC for nonlinear systems Part I: The model-based case
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We develop a tracking model predictive control (MPC) scheme for nonlinear systems using the linearized dynamics at the current state as a prediction model. Under reasonable assumpt…
Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems
Victor G. Lopez, Mohammad Alsalti, Matthias A. Müller
This paper introduces and analyzes an improved Q-learning algorithm for discrete-time linear time-invariant systems. The proposed method does not require any knowledge of the syste…
On the design of terminal ingredients for data-driven MPC
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a term…
Constrained nonlinear output regulation using model predictive control -- extended version
Johannes Köhler, Matthias A. Müller, Frank Allgöwer
We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the applicati…