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Cédric Join

Bio: Cédric Join is an academic researcher from University of Lorraine. The author has contributed to research in topics: Nonlinear system & Fault detection and isolation. The author has an hindex of 32, co-authored 178 publications receiving 4562 citations. Previous affiliations of Cédric Join include Nancy-Université & Concordia University Wisconsin.


Papers
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07 Sep 2009
TL;DR: In this article, the authors considered the problem of control and actuator fault-tolerance for a satellite system in a rendezvous mission, where the mission consists in capturing a sample (called target) using a chaser satellite.
Abstract: This work treats the problem of control and actuator fault-tolerance for a satellite system in a rendezvous mission. The mission consists in capturing a sample (called target) using a chaser satellite. The study considers the position and attitude dynamics of the chaser equipped with 12 thrusters. Firstly, a controller is designed for the chaser using flatness. The controller aims to move the system from an initial position/attitude to the position of the target with the adequate attitude. Since the chaser is an over-actuated system, a control allocation scheme is used to allocate over the thrusters, the efforts and moments generated by the controller. Secondly, actuator faults are considered. The chaser's thrusters are subject to two types of faults: jamming in closed position and jamming in open position. Using actuator redundancy, a control re-allocation scheme is used to accommodate actuator faults. The efficiency of the control and the FTC approaches are investigated in simulation.

2 citations

Posted Content
23 Jun 2021
TL;DR: In this paper, Proportional-Derivative feedback loops, or iPDs, were proposed to replace PIs and PIDs, which play a key role in control engineering.
Abstract: This paper suggests to replace PIs and PIDs, which play a key role in control engineering, by intelligent Proportional-Derivative feedback loops, or iPDs, which are derived from model-free control. This standpoint is enhanced by a laboratory experiment.

2 citations

Journal Article
TL;DR: When compared to the commercial “Auto-Scaling” algorithms, this easily implementable approach, behaves better even with sharp workload fluctuations, and is confirmed by experiments on Amazon Web Services (AWS).
Abstract: In cloud computing management, the dynamic adaptation of computing resource allocations under timevarying workload is an active domain of investigation. Several control strategies were already proposed. Here the modelfree control setting and the corresponding “intelligent” controllers, which are most successful in many concrete engineering situations, are employed for the “horizontal elasticity.” When compared to the commercial “Auto-Scaling” algorithms, our easily implementable approach, behaves better even with sharp workload fluctuations. This is confirmed by experiments on Amazon Web Services (AWS).

1 citations

17 Mar 2009
TL;DR: In this paper, a reformulation and extension of the diagnostic technique used by Fluidess and Sira-Ramirez for the identification of defauts in lineaires is presented.
Abstract: Cet article presente une reformulation et extension au diagnostic des methodes algebrique developpees pour l'identification des systemes lineaires (Fliess and Sira-Ramirez [2003]). En exploitant les outils et resultats de l'analyse pseudo-spectrale, une approche est proposee pour la generation d'indicateurs de defaut. Le principal atout de cette approche est qu'il est possible, sous certaines hypotheses, de detecter, localiser et identifier les defauts uniquement a partir des mesures de la commande et celles de la sortie sans identifier explicitement un modele. Un exemple numerique est fourni et commente afin d'illustrer l'approche proposee.

1 citations

Posted Content
TL;DR: In this article, a new setting on time series is proposed, which allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by the concrete data, may be abandoned.
Abstract: Electricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient and easily implementable. Numerous computer simulations are presented.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: A bibliographical review on reconfigurable fault-tolerant control systems (FTCS) is presented, with emphasis on the reconfiguring/restructurable controller design techniques.

2,455 citations

Book ChapterDOI
15 Feb 2011

1,876 citations

01 Nov 1981
TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

1,829 citations