Concept
System identification
Related publications (118)
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 ...
IEEE2023Unconstrained 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 York2023Data-driven approaches for non-invasive cuffless blood pressure monitoring
Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...
EPFL2023Achieving 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 ...
Piscataway2023Data-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 ...
IEEE2023Closed-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 LTD2022Dynamic 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