13 papers
Graph Neural Model Predictive Control for High-Dimensional Systems
Patrick Benito Eberhard, Luis Pabon, Daniele Gammelli +5
The control of high-dimensional systems, such as soft robots, requires models that faithfully capture complex dynamics while remaining computationally tractable. This work presents…
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…
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…
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…