Publications related to Recurrent Neural Network based MPC for Process Industries

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Alireza Karimi, Mert Eyuboglu, Nathan Russell Powell

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

2024

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

Colin Neil Jones, Bratislav Svetozarevic, Wenjie Xu

We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...

Elsevier Sci Ltd2024

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Nikolaos Geroliminis, Can Chen

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

Informs2024

Data-driven LPV Control for Micro-disturbance Rejection in a Hybrid Isolation Platform

Alireza Karimi, Elias Sebastian Klauser

A novel approach for linear parameter-varying (LPV) controller synthesis for adaptive rejection of time-varying sinusoidal disturbances is proposed. Only the frequency response data of a linear time-invariant (LTI) multiple-input multiple-output (MIMO) sys ...

2024

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Alireza Karimi, Mert Eyuboglu

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

2024

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Alexander Carl Unnervik

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Incorporating Projective Geometry into Deep Learning

Michal Jan Tyszkiewicz

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Tianyue Chen, Michele Bianco

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Recurrent Neural Network based MPC for Process Industries

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Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Data-driven LPV Control for Micro-disturbance Rejection in a Hybrid Isolation Platform

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Incorporating Projective Geometry into Deep Learning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Data-driven LPV Control for Micro-disturbance Rejection in a Hybrid Isolation Platform

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Incorporating Projective Geometry into Deep Learning