Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees

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Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media

Demetri Psaltis, Christophe Moser, Navid Borhani, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin

The output of physical systems, such as the scrambled pattern formed by shining the spot of a laser pointer through fog, is often easily accessible by direct measurements. However, selection of the input of such a system to obtain a desired output is diffi ...

SPRINGERNATURE2020

Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks

Nikolaos Geroliminis, Isik Ilber Sirmatel, Ye Ren

Most perimeter control methods in literature are the model-based schemes designing the controller based on the available accurate macroscopic fundamental diagram (MFD) function with well known techniques of modern control methods. However, accurate modelin ...

2020

Data-driven methods for building control - A review and promising future directions

Colin Neil Jones, Yingzhao Lian, Emilio Maddalena

A review of the heating, ventilation and air-conditioning control problem for buildings is presented with particular emphasis on its distinguishing features. Next, we not only examine how data-driven algorithms have been exploited to tackle the main challe ...

2020

Multi-Region Network Perimeter Control via Model Free Adaptive Iterative Learning Control

Nikolaos Geroliminis, Isik Ilber Sirmatel, Ye Ren

Most perimeter control methods in literature are the model-based schemes. However, accurate modeling of the traffic flow system is hard and time-consuming. On the other hand, macroscopic traffic flow patterns show heavily similarity between days, and data ...

2020

Model predictive perimeter control of MFD networks with guaranteed stability

Nikolaos Geroliminis, Isik Ilber Sirmatel

Traffic management for large-scale urban road networks remains a challenging problem. Aggregated dynamical models of city-scale traffic, based on the macroscopic fundamental diagram (MFD), enable development of model-based control schemes for use with the ...

2020

On Gaussian Process Based Koopman Operators

Colin Neil Jones

Enabling analysis of non-linear systems in linear form, the Koopman operator has been shown to be a powerful tool for system identification and controller design. However, current data-driven methods cannot provide quantification of model uncertainty given ...

ELSEVIER2020

Optimization-based Control, Estimation, and Identification of Urban Road Transport Systems

Isik Ilber Sirmatel

Urbanization intensifies as a global trend, exposing transportation networks to ever increasing levels of congestion. As network usage increases with available infrastructure, building new roads is not a solution. Design of intelligent transportation syste ...

EPFL2020

Data-Driven Distributed Reactive Power Sharing in Microgrids

Alireza Karimi, Seyed Sohail Madani

In this paper, reactive power sharing for Photovoltaic (PV) units in islanded microgrids has been formulated as a robust control design problem and is solved using convex optimization method. In addition to reactive power sharing, the disturbance rejection ...

IEEE2020

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Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media

Demetri Psaltis, Christophe Moser, Navid Borhani, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin

SPRINGERNATURE2020