Nouvelle approche pour l’optimisation de systèmes mécaniques en vue de la récupération d'énergie vibratoire

La récupération d’énergie à partir des vibrations mécaniques est une préoccupation importante à l’heure actuelle car elle permet de rendre autonome les systèmes de surveillance vibratoire ou de contrôle de vibration (semi-actif). Cet article se positionne sur le thème de la récupération d’énergie vibratoire et plus particulièrement, dans la phase de conception d’un tel système, lors de l’étape de la « transformation et l’optimisation mécanique ». Dans ce sens, l’article propose une méthode d’aide à la conception des résonateurs équipant les systèmes de récupération. Cette méthode utilise les fonctions habituelles des interfaces (débattement, isolation) plus une fonction récupération d’énergie. La démarche intègre une étape supplémentaire aux démarches classiques de mise sous forme adimensionnelle de ces fonctions afin de minimiser le nombre de paramètres de plus haut niveau à utiliser lors d’une optimisation globale

Estimation of plate elastic moduli through vibration testing

International audienceThis paper considers the identification problem for 2D-structures by comparing a modal method with a new method based on the estimation of the dispersion equation in k-space. Both methods are validated by numerical simulation and by measurements based on an acoustic holography experiment

Contribution à l'estimation robuste de modèles dynamiques (Application à la commande de systèmes dynamiques complexes.)

L'identification des systèmes dynamiques complexes reste une préoccupation lorsque les erreurs de prédictions contiennent des outliers d'innovation. Ils ont pour effet de détériorer le modèle estimé, si le critère d'estimation est mal choisi et mal adapté. Cela a pour conséquences de contaminer la distribution de ces erreurs, laquelle présente des queues épaisses et s'écarte de la distribution normale. Pour résoudre ce problème, il existe une classe d'estimateurs, dits robustes, moins sensibles aux outliers, qui traitent d'une manière plus douce la transition entre résidus de niveaux très différents. Les M-estimateurs de Huber font partie de cette classe. Ils sont associés à un mélange des normes L2 et L1, liés à un modèle de distribution gaussienne perturbée, dit gross error model. A partir de ce cadre formel, nous proposons dans cette thèse, un ensemble d'outils d'estimation et de validation de modèles paramétriques linéaires et pseudo-linéaires boîte-noires, avec extension de l'intervalle de bruit dans les petites valeurs de la constante d'accord de la norme de Huber. Nous présentons ainsi les propriétés de convergence du critère d'estimation et de l'estimateur robuste. Nous montrons que l'extension de l'intervalle de bruit réduit la sensibilité du biais de l'estimateur et améliore la robustesse aux points de levage. Pour un type de modèle pseudo-linéaire, il est présenté un nouveau contexte dit L-FTE, avec une nouvelle méthode de détermination de L, dans le but d'établir les linéarisations du gradient et du Hessien du critère d'estimation, ainsi que de la matrice de covariance asymptotique de l'estimateur. De ces relations, une version robuste du critère de validation FPE est établie et nous proposons un nouvel outil d'aide au choix de modèle estimé. Des expérimentations sur des processus simulés et réels sont présentées et analysées.L'identification des systèmes dynamiques complexes reste une préoccupation lorsque les erreurs de prédictions contiennent des outliers d'innovation. Ils ont pour effet de détériorer le modèle estimé, si le critère d'estimation est mal choisi et mal adapté. Cela a pour conséquences de contaminer la distribution de ces erreurs, laquelle présente des queues épaisses et s'écarte de la distribution normale. Pour résoudre ce problème, il existe une classe d'estimateurs, dits robustes, moins sensibles aux outliers, qui traitent d'une manière plus douce la transition entre résidus de niveaux très différents. Les M-estimateurs de Huber font partie de cette classe. Ils sont associés à un mélange des normes L2 et L1, liés à un modèle de distribution gaussienne perturbée, dit gross error model. A partir de ce cadre formel, nous proposons dans cette thèse, un ensemble d'outils d'estimation et de validation de modèles paramétriques linéaires et pseudo-linéaires boîte-noires, avec extension de l'intervalle de bruit dans les petites valeurs de la constante d'accord de la norme de Huber. Nous présentons ainsi les propriétés de convergence du critère d'estimation et de l'estimateur robuste. Nous montrons que l'extension de l'intervalle de bruit réduit la sensibilité du biais de l'estimateur et améliore la robustesse aux points de levage. Pour un type de modèle pseudo-linéaire, il est présenté un nouveau contexte dit L-FTE, avec une nouvelle méthode de détermination de L, dans le but d'établir les linéarisations du gradient et du Hessien du critère d'estimation, ainsi que de la matrice de covariance asymptotique de l'estimateur. De ces relations, une version robuste du critère de validation FPE est établie et nous proposons un nouvel outil d'aide au choix de modèle estimé. Des expérimentations sur des processus simulés et réels sont présentées et analysées.Complex dynamic systems identification remains a concern when prediction errors contain innovation outliers. They have the effect to damage the estimated model if the estimation criterion is badly chosen and badly adapted. The consequence is the contamination of the distribution of these errors; this distribution presents heavy tails and deviates of the normal distribution. To solve this problem, there is a robust estimator's class, less sensitive to the outliers, which treat the transition between residuals of very different levels in a softer way. The Huber's M-estimators belong to this class. They are associated to a mixed L2 - L1 norm, related to a disturbed Gaussian distribution model, namely gross error model. From this formal context, in this thesis we propose a set of estimation and validation tools of black-box linear and pseudo-linear models, with extension of the noise interval to low values of the tuning constant in the Huber's norm. We present the convergence properties of the robust estimation criterion and the robust estimator. We show that the extension of the noise interval reduces the sensitivity of the bias of the estimator and improves the robustness to the leverage points. Moreover, for a pseudo-linear model structure, we present a new context, named L-FTE, with a new method to determine L, in order to linearize the gradient and the Hessien of estimation criterion and the asymptotic covariance matrix of the estimator. From these expressions, a robust version of the FPE validation criterion is established and we propose a new decisional tool for the estimated model choice. Experiments on simulated and real systems are presented and analyzed.PARIS-Arts et Métiers (751132303) / SudocSudocFranceF

Maximum power point tracking using P&O control optimized by a neural network approach: a good compromise between accuracy and complexity

In the field of power optimization of photovoltaic panels (PV), there exist many maximum power point tracking (MPPT) control algorithms, such as: the perturb and observe (P&O) one, the algorithms based on fuzzy logic and the ones using a neural network approaches. Among these MPPT control algorithms, P&O is one of the most widely used due to its simplicity of implementation. However, the major drawback of this kind of algorithm is the lack of accuracy due to oscillations around the PPM. Conversely, MPPT control using neural networks have shown to be a very efficient solution in term of accuracy. However, this approach remains complex. In this paper we propose an original optimization of the P&O MPPT control with a neural network algorithm leading to a significant reduction of the computational cost required to train it, ensuring a good compromise between accuracy and complexity. The algorithm has been applied to the models of two different types of solar panels, which have been experimentally validated

Design methodology of a complex CKC mechanical joint with a representation energetic tool multi-Bond graph: application to the helicopter

Due to the operation of the rotor, the helicopter is subject to important vibration levels affecting namely the fatigue of the mechanical parts and the passenger comfort. Suspensions between the main gear box (MGB) and the fuselage help to filter theses problematic vibrations. Their design can be difficult since the filtering should be efficient for different types of external forces (pumping force and roll/pitch torque) which may appear during the flight. As passive solutions classically show their limits, intelligent active solutions are proposed so that the filtering can be adjusted according to the vibration sources. Such studies still suffer from a lack of tools and methods, firstly, necessary to the design of complex mechanical systems (due to their multi-phase multi-physics multi-interaction characteristic, ...) and secondly, to develop of an intelligent joint. The main objective of this chapter is to provide a methodology for designing and analyzing an intelligent joint using an energetic representation approach: the multibond graph (MBG). This method is applied here to a complex mechanical system with closed kinematic chains (CKC) which is the joint between the main gear box (MGB) and the aircraft structure of a helicopter. Firstly, the MBG method is analyzed. Secondly, after a brief state of art of the MGB-Fuselage joint, developments focus on the 2D and 3D modeling of the MGB-Fuselage joint with a MBG approach. The 20-sim software is used to conduct the simulation of bond graph. Finally, the MBG models results are presented, illustrating the potential of the MBG tool to predict the dynamic of a complex CKC mechanical system.Chaire de la fondation d'entreprises EAD

Identification de systèmes utilisant les réseaux de neurones (un compromis entre précision, complexité et charge de calculs)

Ce rapport porte sur le sujet de recherche de l'identification boîte noire du système non linéaire. En effet, parmi toutes les techniques nombreuses et variées développées dans ce domaine de la recherche ces dernières décennies, il semble toujours intéressant d'étudier l'approche réseau de neurones dans l'estimation de modèle de système complexe. Même si des modèles précis ont été obtenus, les principaux inconvénients de ces techniques restent le grand nombre de paramètres nécessaires et, en conséquence, le coût important de calcul nécessaire pour obtenir le niveau de pratique de la précision du modèle désiré. Par conséquent, motivés pour remédier à ces inconvénients, nous avons atteint une méthodologie complète et efficace du système d'identification offrant une précision équilibrée, la complexité et les modèles de coûts en proposant, d'une part, de nouvelles structures de réseaux de neurones particulièrement adapté à une utilisation très large en matière de modélisation système pratique non linéaire, d'autre part, un simple et efficace technique de réduction de modèle, et, troisièmement, une procédure de réduction de coût de calcul. Il est important de noter que ces deux dernières techniques de réduction peut être appliquée à une très large gamme d'architectures de réseaux de neurones sous deux simples hypothèses spécifiques qui ne sont pas du tout contraignant. Enfin, la dernière contribution importante de ce travail est d'avoir montré que cette phase d'estimation peut être obtenue dans un cadre robuste si la qualité des données d'identification qu'il oblige. Afin de valider la procédure d'identification système proposé, des exemples d'applications entraînées en simulation et sur un procédé réel, de manière satisfaisante validé toutes les contributions de cette thèse, confirmant tout l'intérêt de ce travail.This report concerns the research topic of black box nonlinear system identification. In effect, among all the various and numerous techniques developed in this field of research these last decades, it seems still interesting to investigate the neural network approach in complex system model estimation. Even if accurate models have been derived, the main drawbacks of these techniques remain the large number of parameters required and, as a consequence, the important computational cost necessary to obtain the convenient level of the model accuracy desired. Hence, motivated to address these drawbacks, we achieved a complete and efficient system identification methodology providing balanced accuracy, complexity and cost models by proposing, firstly, new neural network structures particularly adapted to a very wide use in practical nonlinear system modeling, secondly, a simple and efficient model reduction technique, and, thirdly, a computational cost reduction procedure. It is important to notice that these last two reduction techniques can be applied to a very large range of neural network architectures under two simple specific assumptions which are not at all restricting. Finally, the last important contribution of this work is to have shown that this estimation phase can be achieved in a robust framework if the quality of identification data compels it. In order to validate the proposed system identification procedure, application examples driven in simulation and on a real process, satisfactorily validated all the contributions of this thesis, confirming all the interest of this work.PARIS-Arts et Métiers (751132303) / SudocSudocFranceF

Simulation of a helicopter’s main gearbox semiactive suspension with bond graphs

This paper presents a bond graph model of a helicopter’s semiactive suspension and the associated simulations. The structural and modular approach proposed with bond graph permits a systematic modeling of mechatronic multibody systems. This approach was carried out thanks to the use of the singular perturbation method, which is a variant of penalty formulation. The model is then built as an assembly of components or modules (rigid bodies and compliant kinematic joints) by following the structure of the actual system. The bond graph model of the passive suspension with fixed flapping masses has been verified with another multibody tool for three different excitations (pumping, roll, and yaw). Next, the passive model, augmented with electrical actuators and controllers, is called the semiactive suspension model. Simulations on the semiactive suspension model have been conducted.Chaire "dynamique des systèmes complexes" - Fondation EAD

Bond Graph Modeling and Simulation of a Vibration Absorber System in Helicopters

n the last 20 years, computer science has considerably progressed and there has been a resurgence of interest in bond graphs. The evolution of bond graph software has allowed for the full exploitation of its graphical aspects and for its simulation directly from the modeling environment without the need for the modeler to derive the associated dynamic equations. However, within this last decade, few simulations of complex multibody systems modeled with bond graphs have been conducted directly from a graphic software platform. In this context, the objective of this chapter is to show how bond graphs can be used to model and simulate a complex mechatronic system with bond graph simulation software. The multibody system studied in this chapter is a helicopter’s vibration absorber suspension. The structural and modular approach allowed by bond graphs permits a systematic modeling of mechatronic multibody systems. The model is then built as an assembly of components or modules (rigid bodies and compliant kinematic joints) by following the structure of the actual system. This approach was carried out with the use of the parasitic elements method. The bond graph model of the suspension has been verified with another multibody tool for three different excitations (pumping, roll, and yaw). The first part of this chapter will be dedicated to giving the reader an overview of the modeling of multibody systems with bond graphs. BG models of the rigid body and all the basic kinematic joints will be presented. The main existing methods (zero-causal paths ZCPs, Lagrange multipliers, and singular perturbation) for modeling and carrying out simulations of multibody systems will be recalled. The second part of this chapter will present a vector bond graph (also called multibond graph) model of a helicopter’s antivibratory system and the associated simulations. This system is a specific suspension of a helicopter, which filters the vibration coming from the rotor to the fuselage. It is a complex multibody system with four closed kinematic chains (CKC). The dynamic equations of such a CKC system are differential-algebraic equation systems (DAE) that are often difficult to treat and which require specific solving methods. The intention of writing this chapter was to give to bond graph practitioners a detailed and comprehensive method so as to model and conduct simulations of complex multibody systems directly from a bond graph modeling interface.Chaire "dynamique des systèmes complexes" - Fondation d'entreprises EAD

Control loads reduction through control system architecture optimization – application to a conventional rotor on compound helicopters

A kinematic study of a helicopter main rotor control system is carried out to investigate loads in servo actuators and non-rotating scissors during high speed and high load factors maneuvers. The kinematic model is then used to optimize the servo-actuators placement and pre-inclination in order to minimize static and dynamic loads in the three servo-actuators and in the non-rotating scissors. The inputs for the model (blade pitch link loads and pilot input to trim the aircraft) are taken from flight tests measurements, current rotor computations being unable to predict blade root torsion moments vs azimuth with enough accuracy. The analysis is based on X3 demonstrator flight tests, which showed high control system loads that used to reduce flight envelope during the first flight test campaign. Flight tests measurements are used to validate the kinematic model used for the optimization. Computations made for X3 case at 220kts showed a reduction of 40% of maximum static load and 45% of maximum dynamic load on servo-actuators compared to the initial placement of the servo actuators. With appropriate servo actuators pre-inclination, dynamic loads in the non-rotating scissors are decreased by 95% at high speed trim flight. This paper shows how it is possible to keep a conventional rotor control system for compound helicopters. The optimization algorithm presented in this paper can be used for conventional helicopters to reduce loads in the control system and then limit command reinjection because of control system flexibility, and on compound helicopters to expand the flight envelope and to remove control system loads as the first limit factors at high speed

Maximum power point tracking using P&O control optimized by a neural network approach: a good compromise between accuracy and complexity

In the field of power optimization of photovoltaic panels (PV), there exist many maximum power point tracking (MPPT) control algorithms, such as: the perturb and observe (P&O) one, the algorithms based on fuzzy logic and the ones using a neural network approaches. Among these MPPT control algorithms, P&O is one of the most widely used due to its simplicity of implementation. However, the major drawback of this kind of algorithm is the lack of accuracy due to oscillations around the PPM. Conversely, MPPT control using neural networks have shown to be a very efficient solution in term of accuracy. However, this approach remains complex. In this paper we propose an original optimization of the P&O MPPT control with a neural network algorithm leading to a significant reduction of the computational cost required to train it, ensuring a good compromise between accuracy and complexity. The algorithm has been applied to the models of two different types of solar panels, which have been experimentally validated