Distributed state estimation for discrete-time linear time invariant systems: A survey

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The goal of this thesis is to propose pragmatic solutions to real challenges faced in the industry. The scope of this thesis encompasses two subjects: frequency-based structured controller synthesis for linear time-invariant (LTI) systems on one side, and ...

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 ...

In recent years, numerical simulations of hemodynamics have gained significant attention within the medical community, thanks to their ability of non-invasively estimating the blood flow conditions. However, high-fidelity simulations require extensive comp ...

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

A novel approach for robust controller synthesis, which models uncertainty as an elliptical set, is proposed in the paper. Given a set of frequency response functions of linear time-invariant (LTI) multiple-input multiple-output (MIMO) systems, the approac ...

This article addresses the problem of simultaneous distributed state estimation, and control of linear systems with linear state feedback, subjected to process, and measurement noise, under the constraints of quantized, and rate-limited network data transm ...

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 ...

Generalized sampling consists in the recovery of a function f, from the samples of the responses of a collection of linear shift-invariant systems to the input f . The reconstructed function is typically a member of a finitely generated integer-shift invar ...

Bayesian state estimation in the form of Kalman smoothing was applied to differential mobility analyser train (DMA-train) measurements of aerosol size distribution dynamics. Four experiments were analysed in order to estimate the aerosol size distribution, ...

Simulation script for the paper "Regularization for distributionally robust state estimation and prediction". Run tests/test_cdc.py to reproduce results. Extended versions can be found at https://github.com/DecodEPFL/. ...

We consider a class of spreading processes on networks, which generalize commonly used epidemic models such as the SIR model or the SIS model with a bounded number of reinfections. We analyze the related problem of inference of the dynamics based on its pa ...

We propose a principled method for projecting an arbitrary square matrix to the non- convex set of asymptotically stable matrices. Leveraging ideas from large deviations theory, we show that this projection is optimal in an information-theoretic sense and ...

The wireless sensor nodes used for monitoring the condition of grid equipment always be powered by disposable batteries. However, it introduces disadvantages, such as inconvenient replacement, short lifespan, and envi-ronmental pollution, significantly imp ...

Is it possible to detect if the sample paths of a stochastic process almost surely admit a finite expansion with respect to some/any basis? The determination is to be made on the basis of a finite collection of discretely/noisily observed sample paths. We ...

Wireless sensor nodes (WSNs) are generally powered by batteries, which results in a substantial limitation to the places where the nodes can be installed, to the maximum number of deployable devices, and to the node lifetime. To meet the demand for Interne ...

After more than thirty years of continuous research and development, the performances of conventional Brillouin based distributed optical fiber sensors (DOFS) are now peaking, as they are facing fundamental barriers that restrict the power of the light inj ...

This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

Many engineering fields rely on frequency-domain dynamical systems for the mathematical modeling of physical (electrical/mechanical/etc.) structures. With the growing need for more accurate and reliable results, the computational burden incurred by frequen ...

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 ...

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 ...

Distributed state estimation for discrete-time linear time invariant systems: A survey

Chattez avec Graph Search

Frequency domain data-driven robust and optimal control

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

Reduced order models to address temporal complexity and geometrical variability in hemodynamics

Regularization for distributionally robust state estimation and prediction

Data-driven IQC-Based Robust Control Design for Hybrid Micro-disturbance Isolation Platform

A Distributed Luenberger Observer for Linear State Feedback Systems With Quantized and Rate-Limited Communications

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

Shortest-support multi-spline bases for generalized sampling

Aerosol formation and growth rates from chamber experiments using Kalman smoothing

Regularization for distributionally robust state estimation and prediction

Bayes-optimal inference for spreading processes on random networks

Efficient Learning of a Linear Dynamical System with Stability Guarantees

Self-powered transformer intelligent wireless temperature monitoring system based on an ultra-low acceleration piezoelectric vibration energy harvester

Detecting whether a stochastic process is finitely expressed in a basis

An Energy-Autonomous Wireless Sensor With Simultaneous Energy Harvesting and Ambient Light Sensing

Advanced methods for Brillouin based distributed optical fiber sensors

Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

Model order reduction based on functional rational approximants for parametric PDEs with meromorphic structure

Data-Driven Unknown-Input Observers and State Estimation

A Note on BIBO Stability