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.

Robust residual generation for linear fault diagnosis: an algebraic setting with examples

Abstract: We are generating residuals for linear fault diagnosis and isolation which are 1. robust with respect to a large variety of additive disturbances, A reader who is already familiar with this algebraic setting may directly proceed to § 3. The numerous examples on various aspects of diagnosis, like the previous one, are written on the other hand in such a way that they may be understood by anyone who is not mastering those mathematical tools. We hope therefore that our results might be understood by a large community. 2. working in closed-loop, even with uncertain parameters. Several examples with their computer simulations, including a concrete case-study of a two mass system, are enlightening our viewpoint. Our methods, which are mainly of algebraic flavour (module theory, differential algebra, and operational calculus), are borrowed from recent works on control and identification.

Commande sans modèle et commande à modèle restreint

Abstract: We are introducing a model-free control and a control with a restricted model for finite-dimensional complex systems. This control design may be viewed as a contribution to ``intelligent'' PID controllers, the tuning of which becomes quite easy, even with highly nonlinear and/or time-varying systems. Our main tool is a newly developed numerical differentiator. Differential algebra provides the theoretical framework. Our approach is validated by several numerical experiments.

Actuator Fault Tolerant Control Design Based on a Reconfigurable Reference Input

Abstract: The prospective work reported in this paper explores a new approach to enhance the performance of an active fault tolerant control system. The proposed technique is based on a modified recovery/trajectory control system in which a reconfigurable reference input is considered when performance degradation occurs in the system due to faults in actuator dynamics. An added value of this work is to reduce the energy spent to achieve the desired closed-loop performance. This work is justified by the need of maintaining a reliable system in a dynamical way in order to achieve a mission by an autonomous system, e.g., a launcher, a satellite, a submarine, etc. The effectiveness is illustrated using a three-tank system for slowly varying reference inputs corrupted by actuators faults.

Active fault-tolerant control system design with trajectory re-planning against actuator faults and saturation: Application to a quadrotor unmanned aerial vehicle

Abstract: During the past 30 years, various fault-tolerant control (FTC) methods have been developed to address actuator or component faults for various systems with or without tracking control objectives. However, very few FTC strategies establish a relation between the post-fault reference trajectory to track and the remaining resources in the system after fault occurrence. This is an open problem that is not well considered in the literature. The main contribution of this paper is in the design of a reconfigurable FTC and trajectory planning scheme with emphasis on online decision making using differential flatness. In the fault-free case and on the basis of the available actuator resources, the reference trajectories are synthesized so as to drive the system as fast as possible to its desired setpoint without violating system constraints. In the fault case, the proposed active FTC system (AFTCS) consists in synthesizing a reconfigurable feedback control along with a modified reference trajectories once an actuator fault has been diagnosed by a fault detection and diagnosis scheme, which uses a parameter-estimation-based unscented Kalman filter. Benefited with the integration of trajectory re-planning using the flatness concept and the compensation-based reconfigurable controller, both faults and saturation in actuators can be handled effectively with the proposed AFTCS design. Advantages and limitations of the proposed AFTCS are illustrated using an experimental quadrotor unmanned aerial vehicle testbed

Linear systems

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: