Publications related to A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

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

Giancarlo Ferrari Trecate, Luca Furieri, Baiwei Guo, Andrea Martin

How can we synthesize a safe and near-optimal control policy for a partially-observed system, if all we are given is one historical input/output trajectory that has been corrupted by noise? To address this challenge, we suggest a novel data-driven controll ...

2021

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 ...

PERGAMON-ELSEVIER SCIENCE LTD2020

First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

Maryam Kamgarpour, Luca Furieri

We study the distributed Linear Quadratic Gaussian (LQG) control problem in discrete-time and finite-horizon, where the controller depends linearly on the history of the outputs and it is required to lie in a given subspace, e.g. to possess a certain spars ...

IEEE2020

Data-Driven Passivity-Based Current Controller Design for Power-Electronic Converters of Traction Systems

Alireza Karimi, Seyed Sohail Madani

In this paper, a data-driven method for controller design with constraints on the positive-realness of closed-loop transfer functions over an arbitrary set of frequencies is proposed. The positive-realness of a closed-loop transfer function is represented ...

IEEE2020

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

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Maryam Kamgarpour, Luca Furieri, Na Li

It is known that the set of internally stabilizing controller

\mathcal{C}_{\text{stab}}

is non-convex, but it admits convex characterizations using certain closed-loop maps: a classical result is the {Youla parameterization}, and two recent notions are t ...

2020

Stabilization and Performance Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

Alireza Karimi, Seyed Sohail Madani

Power-Hardware-in-the-Loop (PHIL) setups have gained high importance in validation of the performance of newly developed instruments and devices with low risk and implementation cost. However, the interconnection of a power hardware with a simulated model ...

2020

On Gaussian Process based Koopman Operator

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 ...

2020

A Note on BIBO Stability

Michaël Unser

The statements on the BIBO stability of continuoustime convolution systems found in engineering textbooks are often either too vague (because of lack of hypotheses) or mathematically incorrect. What is more troubling is that they usually exclude the identi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Improving sound absorption through nonlinear active electroacoustic resonators

Romain Christophe Rémy Fleury, Hervé Lissek, Xinxin Guo

Absorbing airborne noise at frequencies below 300 Hz is a particularly vexing problem due to the absence of natural sound absorbing materials at these frequencies. The prevailing solution for low-frequency sound absorption is the use of passive narrow-band ...

2020

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

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

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

First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

Data-Driven Passivity-Based Current Controller Design for Power-Electronic Converters of Traction Systems

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

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Stabilization and Performance Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

On Gaussian Process based Koopman Operator

A Note on BIBO Stability

Improving sound absorption through nonlinear active electroacoustic resonators

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

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Stabilization and Performance Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

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

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

A Note on BIBO Stability

On Gaussian Process based Koopman Operator

Data-Driven Passivity-Based Current Controller Design for Power-Electronic Converters of Traction Systems

Improving sound absorption through nonlinear active electroacoustic resonators