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20242026
most citedA System Parameterization for Direct Data-Driven Estimator Synthesis

6 citations · 6 across the 7 of their papers we have counts for

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7 papers

eess.SY2026

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…

eess.SY2025

Path-following model predictive control for autonomous e-scooters

David Meister, Robin Strässer, Felix Brändle +6

In order to mitigate economical, ecological, and societal challenges in electric scooter (e-scooter) sharing systems, we develop an autonomous e-scooter prototype. Our vision is to…

eess.SY2025

Data-driven Estimator Synthesis with Instantaneous Noise

Felix Brändle, Frank Allgöwer

Data-driven controller design based on data informativity has gained popularity due to its straightforward applicability, while providing rigorous guarantees. However, applying thi…

eess.SY2025

On the effects of angular acceleration in orientation estimation using inertial measurement units

Felix Brändle, David Meister, Marc Seidel +2

In this paper, we analyze the orientation estimation problem using inertial measurement units. Many estimation algorithms suffer degraded performance when accelerations other than…

eess.SY2024

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,…

eess.SY2024

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…