571 citations · 1.3k across the 123 of their papers we have counts for
24 papers · 1 filter
Learning-based Approximate Model Predictive Control for an Impact Wrench Tool
Mark Benazet, Francesco Ricca, Dario Bralla +2
Learning-based model predictive control has emerged as a powerful approach for handling complex dynamics in mechatronic systems, enabling data-driven performance improvements while…
Statistically Consistent Approximate Model Predictive Control
Elias Milios, Kim P. Wabersich, Felix Berkel +2
Model Predictive Control (MPC) offers rigorous safety and performance guarantees but is computationally intensive. Approximate MPC (AMPC) aims to circumvent this drawback by learni…
Unifying Sequential Quadratic Programming and Linear-Parameter-Varying Algorithms for Real-Time Model Predictive Control
Kristóf Floch, Amon Lahr, Roland Tóth +1
This paper presents a unified framework that connects sequential quadratic programming (SQP) and the iterative linear-parameter-varying model predictive control (LPV-MPC) technique…
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…
Design Principles for Sequence Models via Coefficient Dynamics
Jerome Sieber, Antonio Orvieto, Melanie N. Zeilinger +1
Deep sequence models, ranging from Transformers and State Space Models (SSMs) to more recent approaches such as gated linear RNNs, fundamentally compute outputs as linear combinati…
Eigenvalues as a Metric for Memory Dynamics in Sequence Models
Rahel Rickenbach, Jelena Trisovic, Alexandre Didier +2
While softmax attention drives state-of-the-art performance in sequence modeling, its quadratic complexity motivates linear alternatives such as state space models (SSMs). Structur…
Guaranteed Robust Nonlinear MPC via Disturbance Feedback
Antoine P. Leeman, Johannes Köhler, Melanie N. Zeilinger
Robots must satisfy safety-critical state and input constraints despite disturbances and model mismatch. We introduce a robust model predictive control (RMPC) formulation that is f…
Safe and Near-Optimal Control with Online Dynamics Learning
Manish Prajapat, Johannes Köhler, Melanie N. Zeilinger +1
Achieving both optimality and safety under unknown system dynamics is a central challenge in real-world deployment of agents. To address this, we introduce a notion of maximum safe…
An MPC framework for efficient navigation of mobile robots in cluttered environments
Johannes Köhler, Daniel Zhang, Raffaele Soloperto +2
We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortes…
Predictive Control for Driving under Uncertain Road Geometry Estimated from Onboard Vision
Jelena Trisovic, Andrea Carron, Melanie N. Zeilinger
Autonomous vehicles driving on unknown roads must estimate the road geometry from onboard sensors and follow the resulting reference while respecting road boundaries. When the perc…
ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning
Rahel Rickenbach, Alan A. Lahoud, Erik Schaffernicht +2
The computational burden of model predictive control (MPC) limits its application on real-time systems, such as robots, and often requires the use of short prediction horizons. Thi…
DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning
Rahel Rickenbach, Bruce Lee, René Zurbrügg +2
The integration of large language models (LLMs) with control systems has demonstrated significant potential in various settings, such as task completion with a robotic manipulator.…
Inverse Optimal Control with Constraint Relaxation
Rahel Rickenbach, Amon Lahr, Melanie N. Zeilinger
Inverse optimal control (IOC) is a promising paradigm for learning and mimicking optimal control strategies from capable demonstrators, or gaining a deeper understanding of their i…
Time-Varying Coverage Control: A Distributed Tracker-Planner MPC Framework
Patrick Benito Eberhard, Johannes Köhler, Oliver Hüsser +2
Time-varying coverage control addresses the challenge of coordinating multiple agents covering an environment where regions of interest change over time. This problem has broad app…
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…
Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing
Hao Ma, Sabrina Bodmer, Andrea Carron +2
Diffusion models hold great potential in robotics due to their ability to capture complex, high-dimensional data distributions. However, their lack of constraint-awareness limits t…
Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
Manish Prajapat, Johannes Köhler, Amon Lahr +2
Gaussian Process (GP) regression is shown to be effective for learning unknown dynamics, enabling efficient and safety-aware control strategies across diverse applications. However…
Optimal kernel regression bounds under energy-bounded noise
Amon Lahr, Johannes Köhler, Anna Scampicchio +1
Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical context…
A multiobjective approach to robust predictive control barrier functions for discrete-time systems
Alexandre Didier, Melanie N. Zeilinger
We present an optimisation-based approach to ensure robust asymptotic stability stability of a desired set in the state space of nonlinear dynamical systems, while optimising a gen…
Performance-driven Constrained Optimal Auto-Tuner for MPC
Albert Gassol Puigjaner, Manish Prajapat, Andrea Carron +2
A key challenge in tuning Model Predictive Control (MPC) cost function parameters is to ensure that the system performance stays consistently above a certain threshold. To address…
Stochastic Model Predictive Control for Sub-Gaussian Noise
Yunke Ao, Johannes Köhler, Manish Prajapat +4
We propose a stochastic Model Predictive Control (MPC) framework that ensures closed-loop chance constraint satisfaction for linear systems with general sub-Gaussian process and me…
Gaussian processes for dynamics learning in model predictive control
Anna Scampicchio, Elena Arcari, Amon Lahr +1
Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dyn…
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…