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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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TL;DR: In this paper, a model-free control of building heating systems for energy saving is presented, where the need of any mathematical description has been removed and several convincing computer simulations are presented.
Abstract: The model-based control of building heating systems for energy saving encounters severe physical, mathematical and calibration difficulties in the numerous attempts that has been published until now. This topic is addressed here via a new model-free control setting, where the need of any mathematical description disappears. Several convincing computer simulations are presented. Comparisons with classic PI controllers and flatness-based predictive control are provided.

6 citations

Posted Content
03 Aug 2020
TL;DR: It is shown that it is possible to achieve acrobatic rate control of the UAV, which is beyond the previous standard, and MFC is robust even when the quadrotor is highly damaged.
Abstract: Experimental flight tests are reported about quadrotors UAVs via a recent model-free control (MFC) strategy, which is easily implementable. We show that it is possible to achieve acrobatic rate control of the UAV, which is beyond the previous standard. The same remote controller is tested on two physical vehicles without any re-tuning. It produces in both cases low tracking error. We show that MFC is robust even when the quadrotor is highly damaged. A video footage can be found at: this https URL

6 citations

DOI
13 Jul 2020
TL;DR: This work proposes a novel easy-to-tune control approach that achieves high accuracy trajectory tracking in a wide operation domain, thus being able to mitigate wear and aging effects.
Abstract: Cascade P-PI control systems are the most widespread commercial solutions for machine tool positioning systems. However, friction, backlash and wearing effects significantly degrade their closed-loop behaviour. This works proposes a novel easy-to-tune control approach that achieves high accuracy trajectory tracking in a wide operation domain, thus being able to mitigate wear and aging effects.

6 citations

Journal ArticleDOI
TL;DR: Model-free control and active disturbance rejection control (ADRC) are the most prominent approaches in order to keep the benefits of PID controllers, that are so popular in the industrial world, and attenuating their severe shortcomings as discussed by the authors.
Abstract: In today's literature "Model-Free Control," or MFC, and "Active Disturbance Rejection Control," or ADRC, are the most prominent approaches in order to keep the benefits of PID controllers, that are so popular in the industrial world, and in the same time for attenuating their severe shortcomings. After a brief review of MFC and ADRC, several examples show the superiority of MFC, which permits to tackle most easily a much wider class of systems.

6 citations

Journal ArticleDOI
TL;DR: An algebraic method to fault diagnosis for uncertain linear systems is proposed to realize fault diagnosis only from knowledge of input and output measurements without identifying explicitly model parameters.

6 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