29 citations · 120 across the 27 of their papers we have counts for
19 papers · 1 filter
A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
Angela P. Schoellig, Matthias A. Müller, Sebastian Trimpe +8
Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection…
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…
Conformal Prediction-Based MPC for Stochastic Linear Systems
Lukas Vogel, Andrea Carron, Eleftherios E. Vlahakis +1
We propose a stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown disturbance distributions. Unlike exis…
Multi-Timescale Model Predictive Control for Slow-Fast Systems
Lukas Schroth, Daniel Morton, Amon Lahr +3
Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse applications. While model fidelity is cru…
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…
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…
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control
Amon Lahr, Joshua Näf, Kim P. Wabersich +5
Incorporating learning-based models, such as artificial neural networks or Gaussian processes, into model predictive control (MPC) strategies can significantly improve control perf…
Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors
Haldun Balim, Andrea Carron, Melanie N. Zeilinger +1
We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predicto…
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-…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology
Danilo Saccani, Lorenzo Fagiano, Melanie N. Zeilinger +1
In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints th…
Active Learning-based Model Predictive Coverage Control
Rahel Rickenbach, Johannes Köhler, Anna Scampicchio +2
The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications. However, coverage applications face two major cha…
Stochastic MPC with robustness to bounded parametric uncertainty
Elena Arcari, Andrea Iannelli, Andrea Carron +1
The performance of model-based control techniques strongly depends on the quality of the employed dynamics model. If strong guarantees are desired, it is therefore common to robust…
A predictive safety filter for learning-based racing control
Ben Tearle, Kim P. Wabersich, Andrea Carron +1
The growing need for high-performance controllers in safety-critical applications like autonomous driving has been motivating the development of formal safety verification techniqu…
Volume Control of Low-Cost Ventilator with Automatic Set-Point Adaptation
Lukas Hewing, Marcel Menner, Nikolaos Tachatos +6
This paper considers the control design for a low-cost ventilator that is based on a manual resuscitator bag (also known as AmbuBag) to pump air into the lungs of a patient who is…
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Elena Arcari, Andrea Carron, Melanie N. Zeilinger
Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus cont…
Distributed Safe Learning using an Invariance-based Safety Framework
Andrea Carron, Jerome Sieber, Melanie N. Zeilinger
In large-scale networks of uncertain dynamical systems, where communication is limited and there is a strong interaction among subsystems, learning local models and control policie…
Distributed Model Predictive Safety Certification for Learning-based Control
Simon Muntwiler, Kim P. Wabersich, Andrea Carron +1
While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challeng…
Probabilistic model predictive safety certification for learning-based control
Kim P. Wabersich, Lukas Hewing, Andrea Carron +1
Reinforcement learning (RL) methods have demonstrated their efficiency in simulation environments. However, many applications for which RL offers great potential, such as autonomou…