7 papers
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…
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…
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…
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…
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…