Publications associées à Data-driven IQC-Based Uncertainty Modelling for Robust Control Design

Publications associées (8)

Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens

Lenka Zdeborová, Emanuele Troiani, Vittorio Erba, Luca Biggio

Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the fun ...

American Physical Society2025

A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids

Yuning Jiang

The integration of various power sources, including renewables and electric vehicles, into smart grids is expanding, introducing uncertainties that can result in issues like voltage imbalances, load fluctuations, and power losses. These challenges negative ...

2024

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Ante Maric, Yiming Li, Sylvain Calinon

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Data-driven frequency-based feedforward control design for a robotic arm joint

Alireza Karimi, Philippe Louis Schuchert

Next-generation motion control systems require fast and precise control, but advanced control strategies often rely on complex and expensive models. This paper proposes a data-driven approach to tune controllers for a joint used in a robotic arm. This is a ...

IEEE2024

Towards Robust Vision Models

Harshitha Machiraju

Deep convolutional neural networks (DCNNs) have significantly advanced the field of computer vision and are often considered as plausible models for the human visual system. However, they still have several vulnerabilities in terms of adversarial attacks, ...

EPFL2024

Applying a 2D Variational Rigid Block Modeling Method to Rubble Stone Masonry Walls Considering Uncertainty in Material Properties

Katrin Beyer, Qianqing Wang, Mathias Christian Haindl Carvallo

The computational cost of detailed micro-modeling of masonry structures can be minimized with rigid block analysis methods, where a variational formulation is particularly interesting as it allows for nonlinear pushover analysis. This work investigates the ...

2024

Hydraulic-hydrologic model for the Zambezi River using satellite data and artificial intelligence techniques

José Pedro Gamito de Saldanha Calado Matos

EPFL2014

Computational Aspects of Distributed Optimization in Model Predictive Control

Colin Neil Jones, Melanie Nicole Zeilinger, Tyler Summers

This paper presents a systematic computational study on the performance of distributed optimization in model predictive control (MPC). We consider networks of dynamically coupled systems, which are subject to input and state con- straints. The resulting MP ...

2012