808 citations · 2.6k across the 59 of their papers we have counts for
68 papers
Full-Order Safety for Nonlinear Reduced-Order MPC
Luis Pabon, Johannes Köhler, Revant Kasichainula +1
Reduced-order models (ROMs) make model predictive control (MPC) of high-dimensional nonlinear systems tractable, but the resulting modeling error can lead to constraint violations…
Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements
Yibo Zhou, Johannes Köhler
We present a scalable method for identifying nonlinear dynamics from partial measurements, supporting physics-based, neural-network, and gray-box models. We use the Prediction Erro…
Stability of Certainty-Equivalent Adaptive LQR for Linear Systems with Unknown Time-Varying Parameters
Marcell Bartos, Johannes Köhler, Florian Dörfler +1
Standard model-based control design deteriorates when the system dynamics change during operation. To overcome this challenge, online and adaptive methods have been proposed in the…
Robust reduced-order model predictive control using peak-to-peak analysis of filtered signals
Johannes Köhler, Carlo Scholz, Melanie Zeilinger
We address the design of a model predictive control (MPC) scheme for large-scale linear systems using reduced-order models (ROMs). Our approach uses a ROM, leverages tools from rob…
Robust Convex Model Predictive Control with collision avoidance guarantees for robot manipulators
Bernhard Wullt, Johannes Köhler, Per Mattsson +2
Industrial manipulators typically operate in cluttered environments, where safe motion planning is critical. However, model uncertainties further complicate this task, which leads…
A robust and adaptive MPC formulation for Gaussian process models
Mathieu Dubied, Amon Lahr, Melanie N. Zeilinger +1
In this paper, we present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems affected by bounded disturbances and unmodeled nonlineariti…
Beyond Asymptotics: Targeted exploration with finite-sample guarantees
Janani Venkatasubramanian, Johannes Köhler, Frank Allgöwer
In this paper, we introduce a targeted exploration strategy for the non-asymptotic, finite-time case. The proposed strategy is applicable to uncertain linear time-invariant systems…
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…
Stochastic MPC with Online-optimized Policies and Closed-loop Guarantees
Marcell Bartos, Alexandre Didier, Jerome Sieber +2
This paper proposes a stochastic model predictive control method for linear systems affected by additive Gaussian disturbances that optimizes over disturbance feedback matrices onl…
Robust targeted exploration for systems with non-stochastic disturbances
Janani Venkatasubramanian, Johannes Köhler, Mark Cannon +1
We propose a novel targeted exploration strategy designed specifically for uncertain linear time-invariant systems with energy-bounded disturbances, i.e., without any assumptions o…
From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements
Haldun Balim, Andrea Carron, Melanie N. Zeilinger +1
We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-…
Predictive control for nonlinear stochastic systems: Closed-loop guarantees with unbounded noise
Johannes Köhler, Melanie N. Zeilinger
We present a stochastic model predictive control framework for nonlinear systems subject to unbounded process noise with closed-loop guarantees. First, we provide a conceptual shri…
Embedded Hierarchical MPC for Autonomous Navigation
Dennis Benders, Johannes Köhler, Thijs Niesten +3
To efficiently deploy robotic systems in society, mobile robots must move autonomously and safely through complex environments. Nonlinear model predictive control (MPC) methods pro…
Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation
Pablo Krupa, Johannes Köhler, Antonio Ferramosca +4
This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artifici…
Adaptive tracking MPC for nonlinear systems via online linear system identification
Tatiana Strelnikova, Johannes Köhler, Julian Berberich
This paper presents an adaptive tracking model predictive control (MPC) scheme to control unknown nonlinear systems based on an adaptively estimated linear model. The model is dete…
Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer (-MPC)
Luis Pabon, Johannes Köhler, John Irvin Alora +4
In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance an…
Parallelizable Parametric Nonlinear System Identification via tuning of a Moving Horizon State Estimator
Léo Simpson, Jonas Asprion, Simon Muntwiler +2
This paper introduces a novel optimization-based approach for parametric nonlinear system identification. Building upon the prediction error method framework, traditionally used fo…
Adaptive Economic Model Predictive Control for linear systems with performance guarantees
Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger +2
We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters…
Safe Guaranteed Exploration for Non-linear Systems
Manish Prajapat, Johannes Köhler, Matteo Turchetta +2
Safely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficien…
Fast System Level Synthesis: Robust Model Predictive Control using Riccati Recursions
Antoine P. Leeman, Johannes Köhler, Florian Messerer +3
System level synthesis enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algori…
MHE under parametric uncertainty -- Robust state estimation without informative data
Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger
In this paper, we study joint state and parameter estimation for general nonlinear systems with uncertain parameters and persistent process and measurement noise. In particular, we…
Nonlinear Functional Estimation: Functional Detectability and Full Information Estimation
Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger
We consider the design of functional estimators, i.e., approaches to compute an estimate of a nonlinear function of the state of a general nonlinear dynamical system subject to pro…
Automatic nonlinear MPC approximation with closed-loop guarantees
Abdullah Tokmak, Christian Fiedler, Melanie N. Zeilinger +2
Safety guarantees are vital in many control applications, such as robotics. Model predictive control (MPC) provides a constructive framework for controlling safety-critical systems…
Towards targeted exploration for non-stochastic disturbances
Janani Venkatasubramanian, Johannes Köhler, Mark Cannon +1
We present a novel targeted exploration strategy for linear time-invariant systems without stochastic assumptions on the noise, i.e., without requiring independence or zero mean, a…