1 citations · 1 across the 4 of their papers we have counts for
4 papers
Certainty-equivalent adaptive MPC for uncertain nonlinear systems
Johannes Köhler
We provide a method to design adaptive controllers for nonlinear systems using model predictive control (MPC). By combining a certainty-equivalent MPC formulation with least-mean-s…
Model Predictive Control with Reference Learning for Soft Robotic Intracranial Pressure Waveform Modulation
Fabian Flürenbrock, Yanick Büchel, Johannes Köhler +2
This paper introduces a learning-based control framework for a soft robotic actuator system designed to modulate intracranial pressure (ICP) waveforms, which is essential for study…
Robust MPC for Uncertain Linear Systems -- Combining Model Adaptation and Iterative Learning
Hannes Petrenz, Johannes Köhler, Francesco Borrelli
This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterat…
Online convex optimization for constrained control of nonlinear systems
Marko Nonhoff, Johannes Köhler, Matthias A. Müller
This paper proposes a modular approach that combines the online convex optimization framework and reference governors to solve a constrained control problem featuring time-varying…