Publications related to System identification

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Roland Schwan, Melanie Nicole Zeilinger, Xuan Truong Nghiem

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti, Daniele Martinelli

In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-time. The proposed architecture stems from the combination of Neural Ordinary Differential Equations (Neural ODEs) with the model structure of recently introduced Rec ...

New York2023

Achieving Optimal Performance With Data-Driven Frequency-Based Control Synthesis Methods

Alireza Karimi, Philippe Louis Schuchert

Frequency Response Function (FRF)-based control synthesis methods for Linear Time-Invariant (LTI) systems have been widely used in control theory and industry. Recently, there has been renewed interest in these methods, employing numerical optimization too ...

Piscataway2023

Data-enabled Predictive Control for Empty Vehicle Rebalancing

Nikolaos Geroliminis, Giancarlo Ferrari Trecate, Pengbo Zhu

A critical operational challenge in Mobility-on-demand systems is the problem of imbalance between vehicle supply and passenger demand. However, conventional model-based methods require accurate parametric system models with complex nonlinear dynamics that ...

IEEE2023

Closed-loop data-driven modeling and distributed control for islanded microgrids with input constraints

Alireza Karimi, Seyed Sohail Madani, Dongdong Zheng

In this paper a new nonlinear identification method for microgrids based on neural networks is proposed. The system identification process can be done using the available closed-loop system input/output data recorded during normal operation without additio ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Dynamic Vehicle Drifting With Nonlinear MPC and a Fused Kinematic-Dynamic Bicycle Model

Guillaume Denis Antoine Bellegarda

In this letter we present a versatile trajectory optimization framework that leverages a fused kinematic-dynamic bicycle model for highly dynamic vehicle drifting maneuvers. Our framework can be used online to generate drifting maneuvers, offline to plan d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Data-Driven Unknown-Input Observers and State Estimation

Giancarlo Ferrari Trecate, Mustafa Sahin Turan

Unknown-input observers (UIOs) allow for estimation of the states of an LTI system without knowledge of all inputs. In this letter, we provide a novel data-driven UIO based on behavioral system theory and the result known as Fundamental Lemma proposed by J ...

2022

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Nicolas Henri Bernard Flammarion, Aditya Vardhan Varre, Loucas Pillaud-Vivien

Motivated by the recent successes of neural networks that have the ability to fit the data perfectly \emph{and} generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum predictor fits perfectly inputs ...

2021

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System identification

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Achieving Optimal Performance With Data-Driven Frequency-Based Control Synthesis Methods

Data-enabled Predictive Control for Empty Vehicle Rebalancing

Closed-loop data-driven modeling and distributed control for islanded microgrids with input constraints

Dynamic Vehicle Drifting With Nonlinear MPC and a Fused Kinematic-Dynamic Bicycle Model

Data-Driven Unknown-Input Observers and State Estimation

A Comparison of optimal measurement-system design with engineering judgement for bridge load testing

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Graph Chatbot

Chat with Graph Search

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

A Comparison of optimal measurement-system design with engineering judgement for bridge load testing

Closed-loop data-driven modeling and distributed control for islanded microgrids with input constraints

Achieving Optimal Performance With Data-Driven Frequency-Based Control Synthesis Methods

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Data-enabled Predictive Control for Empty Vehicle Rebalancing

Data-Driven Unknown-Input Observers and State Estimation

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Dynamic Vehicle Drifting With Nonlinear MPC and a Fused Kinematic-Dynamic Bicycle Model