[Show abstract][Hide abstract] ABSTRACT: In order to enhance the performance of electromagnetic interference (EMI) �lters,
it is necessary to identify high-frequency parasitic elements of their passive
components, mainly those related to the coupled inductors. Motivated by this
issue, in this work a realistic high-frequency model is proposed for the coupled
inductors. Actually, using interval analysis in particular the forward-backward
contractor, a set-membership algorithm has been developed to estimate systematically
the parasitic elements linked with the magnetic components. The main
advantages of this algorithm compared to the �tting methods is : the values of
the estimated parameters are always positive and the corrupted data are taken
into account. The comparison of the simulation results and the experimental
data allows to validate the proposed method.
Control Engineering Practice 08/2014; DOI:10.1016/j.conengprac.2014.03.006 · 1.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The goal of this paper is to present a new on-line human recognition system, which is able to classify persons with adaptive abilities using an incremental classifier. The proposed incremental SVM is fast, as its training phase relies on only a few images and it uses the mathematical properties of SVM to update only the needed parts. In our system, first of all, feature extraction and selection are implemented, based on color and texture features (appearance of the person). Then the incremental SVM classifier is introduced to recognize a person from a set of 20 persons in CASIA Gait Database. The proposed incremental classifier is updated step by step when a new frame including a person is presented. With this technique, we achieved a correct classification rate of 98.46%, knowing only 5% of the dataset at the beginning of the experiment. A comparison with a non-incremental technique reaches recognition rate of 99% on the same database. Extended analyses have been carried out and showed that the proposed method can be adapted to on-line setting.
[Show abstract][Hide abstract] ABSTRACT: In this paper, a fault diagnosis architecture based on a dynamical clustering algorithm is developed to detect and isolate faults in wind turbines. The challenge is to deal with different kinds of faults. Constraints on the time of detection are also added in the sense that a fault must be detected as soon as possible. Also, limited historical data corresponding only to normal operating modes are available. Our methodology is based on a data-driven model and is therefore not dependent of the physical models in the wind turbine. It consists of extracting, from sensor measurements, features that are fed into a dynamical clustering algorithm. The latter learns process behaviors characterized by clusters with the ability to update, recursively, the parameters of these clusters. These parameters are used to create detection signals and health indicators used for diagnosis.
2013 IEEE 52nd Annual Conference on Decision and Control (CDC); 12/2013
[Show abstract][Hide abstract] ABSTRACT: The paper presents an online strategy for sensor and/or actuator fault detection and isolation applied to a dam-gallery. A recursive subspace identification algorithm is used to estimate the dam-gallery model parameters. The main contribution consists in developing a specific identification scheme, insensitive to a certain type of faults. That is, the identified parameters are invariant to the faults. A fault estimation procedure is proposed to detect potential faults. The proposed approach appears to be suitable for open channel systems for which the characteristics are not easily measurable.
Control Engineering Practice 06/2013; 21(6):797–806. DOI:10.1016/j.conengprac.2013.02.013 · 1.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A new continuous state observer is derived for discrete-time linear switched systems under the assumptions that neither the continuous state nor the discrete state are known. A specificity of the proposed observer is that, in contrast to the state of the art, it does not require an explicit prior estimation of the discrete state. The key idea of the method consists in minimizing a non-smooth ℓ2ℓ2-norm-based weighted cost functional, constructed from the matrices of all the subsystems regardless of when each of them is active. In the light of recent development in the literature of compressed sensing, the minimized cost functional has the ability to promote sparsity in a way that makes the knowledge of the discrete mode sequence unnecessary.
Systems & Control Letters 02/2013; 62(2):143–151. DOI:10.1016/j.sysconle.2012.11.017 · 2.06 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper, we present a methodology for drift detection and characterization. Our methodology is based on extracting indicators that reflect the health state of a system. It is situated in an architecture of fault diagnosis/prognosis of dynamical system that we present in this paper. A dynamical clustering algorithm is used as a major tool. The feature vectors are clustered and then the parameters of these clusters are updated as each feature vector arrives. The cluster parameters serve to compute indicators for drift detection and characterization. Then, a prognosis block uses these drift indicators to estimate the remaining useful life. The architecture is tested on a case study of a tank system with different scenarios of single and multiple faults, and with different dynamics of drift.
International Conference on Scalable Uncertainty Management, Marburg, Allemagne; 09/2012
[Show abstract][Hide abstract] ABSTRACT: Parasitic parameters in electrical networks are usually sources of intolerant electromagnetic interference in their near environment. In order to understand better the undesirable phenomenon, the values of these unknown parameters must be estimated with a good accuracy. This work shows how interval analysis can help designing set-membership algorithm that is able to solve with numerical guarantee the kind of issue. A simple example, namely second order filter, is studied and our method shows promising performances for dealing with complex circuits.
[Show abstract][Hide abstract] ABSTRACT: Résumé Les simulations multi-agents consistent à utiliser un en-semble d'agents en interaction de manière à reproduire la dynamique et l'évolution des phénomènes que l'on cherche à simuler. Elles sont aujourd'hui une alternative crédible aux simulations classiques basées sur des modèles analy-tiques, mais leur mise en oeuvre reste difficile. Cette tâche est généralement réalisée par le concepteur qui possède une certaine expertise du phénomène à simuler et dispose de données d'observation de ce même phénomène. Dans ce papier, nous proposons une manière originale de traiter l'observation de comportements réels pour la modélisation d'agents simulés en s'appuyant sur des techniques de clus-tering. La faisabilité de notre approche est démontrée au travers d'un exemple de simulation d'activité humaine. Mots Clef Simulation Multi-Agents, Fouille de données, Modélisa-tion de comportements, Conception automatique d'agents, Techniques de segmentation. Abstract The multiagents simulations consist in using a set of inter-acting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. They are consi-dered now as an alternative to the classical simulations based on analytical models. But, their implementation re-mains difficult, particularly in terms of behaviors extrac-tion and agents modelling. This task is usually performed by the designer who has some expertises, using extracted observation data from the process. In this paper, we pro-pose an original way to deal with observations to model agent behaviors based on clustering techniques. The feasi-bility of our approach is demonstrated through a simula-tion example of human activity.
[Show abstract][Hide abstract] ABSTRACT: Incontournables de nombreuses disciplines scientifiques et technologiques (physique, chimie, biologie, économie...). La modélisation permet en effet de formaliser le comportement du processus étudié à l'aide d'une représentation, baptisée " modèle ", à partir de laquelle il est possible de comprendre, commander ou améliorer le fonctionnement du procédé analysé. Il est important de noter que ce champ thématique à caractère pluridisciplinaire (automatique, traitement du signal, statistique, analyse numérique, génie des procédés...) trouve ses applications dans des domaines très variés allant des processus de fabrication aux systèmes de transport, en passant par les processus environnementaux. L'objectif de ce numéro est de rendre compte des travaux récents dans le domaine de la modélisation et de l'identification des systèmes. Il est constitué de dix articles sélectionnés par le comité scientifique parmi les 43 communications présentées lors des troisièmes Journées Identification et Modélisation Expérimentale (JIME'2011) organisées sous l'égide du groupe de travail Identification de Systèmes du GdR MACS à l'École des Mines de Douai en avril 2011. Ces journées avaient pour objectifs de rassembler les acteurs francophones du domaine de l'identification des systèmes et de proposer une image de la recherche en identification et en modélisation expérimentale, grâce à des présentations orales, des sessions posters et des démonstrations logicielles. Les dix articles sélectionnés ont été retravaillés par les auteurs puis ont suivi le processus de relecture de JESA afin de constituer ce numéro. Les articles ainsi réunis permettent de mettre en lumière les derniers développements théoriques dans le domaine de l'identification des systèmes, ainsi que leurs nombreuses applications et interactions avec d'autres communautés scientifiques. Les avancées récentes concernent notamment, le choix optimal du signal d'excitation, l'identification de systèmes non linéaires, l'identification de systèmes bouclés, l'identification de modèles à temps continu pour des domaines variés tels que la robotique ou les bassins versants.
[Show abstract][Hide abstract] ABSTRACT: This paper addresses the problem of driving the state of a linear discrete-time system to zero in minimum time. The inputs are constrained to lie in a bounded and convex set. The solution presented in the paper is based on the observation that the state sequence induced by the minimum-time control sequence is the sparsest possible state sequence over a certain finite horizon. That is, the desired state sequence must contain as many zero vectors as possible, all those zeros corresponding to the highest values of the time index. Hence, by taking advantage of some recent developments in sparse optimization theory, we propose a numerical solution. We show in simulation that the proposed method can effectively solve the minimum-time problem even for multi-inputs linear discrete-time systems.
Control Applications (CCA), 2012 IEEE International Conference on; 01/2012
[Show abstract][Hide abstract] ABSTRACT: The minimum-time control problem consists in finding a control policy that
will drive a given dynamic system from a given initial state to a given target
state (or a set of states) as quickly as possible. This is a well-known
challenging problem in optimal control theory for which closed-form solutions
exist only for a few systems of small dimensions. This paper presents a very
generic solution to the minimum-time problem for arbitrary discrete-time linear
systems. It is a numerical solution based on sparse optimization, that is the
minimization of the number of nonzero elements in the state sequence over a
fixed control horizon. We consider both single input and multiple inputs
systems. An important observation is that, contrary to the continuous-time
case, the minimum-time control for discrete-time systems is not necessarily
[Show abstract][Hide abstract] ABSTRACT: This paper addresses the problem of identifying linear multi-variable models from the input-output data which is corrupted by an unknown, non-centered, and sparse vector error sequence. This problem is sometimes referred to as error correcting problem in coding theory and robust estimation problem in statistics. By taking advantage of some recent developments in sparse optimization theory, we present here a recursive approach. We then show that the proposed identification method can be adapted to estimate parameter matrices of Jump Markov Linear Systems (JMLS), that is, switched linear systems in which the discrete state sequence is a stationary Markov chain. Some numerical simulation results illustrate the potential of the new method.
[Show abstract][Hide abstract] ABSTRACT: In this work, a recursive procedure is derived for the identification of switched linear models from input–output data. Starting from some initial values of the parameter vectors that represent the different submodels, the proposed algorithm alternates between data assignment to submodels and parameter update. At each time instant, the discrete state is determined as the index of the submodel that, in terms of the prediction error (or the posterior error), appears to have most likely generated the regressor vector observed at that instant. Given the estimated discrete state, the associated parameter vector is updated based on recursive least squares or any fast adaptive linear identifier. Convergence of the whole procedure although not theoretically proved, seems to be easily achieved when enough rich data are available. It has been also observed that by appropriately choosing the data assignment criterion, the proposed on-line method can be extended to deal also with the identification of piecewise affine models. Finally, performance is tested through some computer simulations and the modeling of an open channel system.