Automatique
Fractional PID controller of MIMO Gas Metal Arc Welding process.
Gas metal arc welding (GMAW) plays the great importance in the welding industry on account of high flexibility in the welding of different metals, high welding productivity, and automatic run capabilities. This paper focuses on the development of a fractional PID control of gas metal arc welding system, wherein the current and voltage of welding process are controlled using a fractional PID controller, then the system analyzed and the results compared with conventional PID controller show adequate improvement in the efficiency and performance of the proposed controller. Voir les détails
Mots clés : GMAW, MIMO system, P ID controller, Fractional PID controller
WAVELETS AND PRINCIPAL COMPONENT ANALYSIS METHOD FOR VIBRATION MONITORING OF ROTATING MACHINERY
Fault diagnosis is playing today a crucial role in industrial systems. To improve the reliability, safety and efficiency advanced monitoring methods become increasingly important for many systems. Vibration analysis method is essential in improving condition monitoring and fault diagnosis of rotating machinery. Effective utilization of the vibration signals depends upon the effectiveness of the applied signal processing techniques. In this paper, fault diagnosis is performed using a combination between Wavelet Transform (WT) and Principal Component Analysis (PCA). The WT is employed to decompose the vibration signal of measurements data in different frequency bands. The obtained decomposition levels are used as input to the PCA method for fault identification using respectively, the Q-statistic, it is also called Squared Prediction Error (SPE), and the Q-contribution. Clearly, useful information about the fault can be contained in some levels of wavelet decomposition. For this purpose, Q-contribution is used as an evaluation criterion to select the optimal level, which contains the maximum information. Associated to spectral analysis and envelope analysis, it allows clear visualization of fault frequencies. The objective of this method is to obtain the information contained in the measured data. The monitoring results using real sensor measurements from a pilot scale are presented and discussed. Voir les détails
Mots clés : vibration, Fault Diagnosis, Wavelet Analysis, principal component analysis, squared prediction error
Robust Control of Doubly Fed Induction Generator for WindTurbine Under Sub-Synchronous Operation Mode
This paper presents a modeling and a robust control of doubly fed induction generator for wind generation system. The wholesystem is presented in d-q-synchronous reference frame. The regulation of the electromagnetic torque , stator reactive powercontrol and neuronal controller are applied in order to control the rotor currents of the DFIG. For to improve the controllerrobustness, the study is validated through simulation using software Matlab/Simulink, studies on a 1.5 MW DFIG windgeneration system compared with conventional proportional integral controller. Performance and robustness results obtained willbe presented and analyzed. Voir les détails
Mots clés : wind power generation, modeling, Control, doubly fed induction generator, Neuronal controller, performances.
Adaptive Fuzzy Gain Scheduling of PI Controller for control of theWind Energy Conversion Systems
In this work, the Wind Energy Conversion Systems (WECS) based on doubly fed induction generator (DFIG) model is built.First, we consider the vector control strategy of the active and reactive powers in order to ensure an optimum operation. Thewhole system is presented in d-q-synchronous reference frame. After, the design of Adaptive Fuzzy Gain Scheduling ofProportional Integral Controller (AFGPI) for WECS is described, where the optimization by Fuzzy rules is utilized online toadjust the parameters of PI controller based on the error and its first derivative. Finally, the control of the active and reactivepower using fuzzy-PI controller is simulated using software Matlab/Simulink, studies on a 1.5 MW DFIG wind generationsystem compared with conventional proportional integral controller. Performance and robustness results obtained are presentedand analyzed. Voir les détails
Mots clés : wind systems, doubly fed induction generator, fuzzy control, fuzzy gain scheduling control, fuzzy PI control, PI controller.
Modélisation et Commande d’une Chaine de Conversion d’Energie Renouvelable.
The imminent exhaustion and uncontested from fossil resources has motivated researchers worldwide to find alternatives to such resources in order to ensure equilibrium in energy requirements, which continues to grow. So it is in this context fits the work presented in this thesis. It concerns the modeling and the control of wind energy conversion chain based on a double fed induction machine (DFIG). An indirect control of active and reactive power ensuring optimal functioning is presented. Furthermore, because of inevitable variations of the parameters a fuzzy adaptive control and neural control have been proposed to evaluate the performance and robustness of the control overlooked the parametric variations. Voir les détails
Mots clés : éolienne, MADA, Commande adaptative, réseau de neurone, logique floue, performances.
Nonlinear Model Predictive Control of Quadcopter
In this paper, a quadcopter is controlled by a nonlinear model predictive controller, NMPC, for trajectory tracking in presence of an external perturbation. The nonlinear model predictive control was basically confined to slow processes. Applications to fast processes such as robots are rare because the time for the solution may exceed the sampling period. Metaheuristics have been used for solving many difficult problems. In this work, we consider the application of the Particle Swarm Optimization algorithm to the NMPC optimisation problem model applied for the quadcopter tracking trajectory with presence of an external perturbation. Results show that NMPC-PSO provides a fast solution and can be used in real time. Voir les détails
Mots clés : quadcopter, nonlinear model predictive control, Particle Swarm Optimization, tracking trajectory
Etude et extension de I ‘algorithme MRAC aux systèmes multivariables et a retards
Le contrôle adaptatif à modèle de référence (MRAC), représente une des principales configurations utilisées dans les systèmes adaptatifs. Depuis que les problèmes de stabilité et de robustesse ont été partiellement résolus, plusieurs chercheurs ont trace pour objectif. Améliorations des performances d'une part, et la simplification de conception et d'implantation d'autre part. On traitera dans ce travail les différentes méthodes de la commande adaptative des systèmes SISO déterministes. Une extension aux systèmes multivariables et à retard sera envisagée. La validité des algorithmes développés sera testée sur des systèmes réels. Voir les détails
Mots clés : Commande adaptative, modèle de référence, systèmes multivariables
Commande Predictive non Lineaire d’une Station de Production d’eau Froide à Base de Reseaux de Neurones Artificiels
Cet article présente la commande prédictive non linéaire (CPNL) avec une application sur une station de production d’eau froide . La modélisation de la station a été réalisée à l’aide des Réseaux de Neurones Artificielles (RNA). Le développement des modèles RNA des différents composants de la station à savoir compresseur, évaporateur, condenseur et détendeur ont été réalisés séparément et regroupés par la suite pour donner le modèle complet de la station. L’objectif principal de ce travail est de maintenir les sorties de la commande du système de production proches d’une valeur désirée ou alors de poursuivre une référence donnée par la température d’un produit. Pour cela la commande prédictive non linéaire a été implémenté avec comme modèle interne. Le modèle neuronal développé est validé à l’aide de données entrées/sorties de la station. La commande a été réalisée en utilisant comme variable de commande la température et/ou le débit du fluide frigorigène. Voir les détails
Mots clés : Commande prédictive non linéaire (CPNL), station de production d’eau froide, Réseaux de Neurones Artificielles (RNA)
Selection of Wavelet Decomposition Levels for Vibration Monitoring of Rotating Machinery
The vibration signal of a rotating machine always carries the dynamic information of the machine. Its analysis is very useful for the condition monitoring and fault diagnosis. Many signal analysis methods are able to extract useful information from vibration data. In this paper, bearing fault diagnosis is performed using Wavelet Transform (WT) and Parseval’s theorem. The WT is used to decompose the original signal into several signals in order to obtain multiple data series at different resolutions. The fault can be detected from a given level of resolution. For this purpose, Parseval’s theorem is used as an evaluation criterion to select the optimal level. Associated to envelope analysis, it allows clear visualization of fault frequencies. Vibration signals from a pilot scale are used to demonstrate the usefulness of the proposed method. The results of the application in inner and outer races bearing diagnosis are satisfactory. Voir les détails
Mots clés : fault diagnosis; wavelet transform; Parseval’s theorem; bearing.
Study and control of PWM arc welding inverter based on a microcontroller
This paper presents an arc welding inverter, based on a microcontroller device for generate PWM pulse. An IGBT inverter was established by applying several control techniques. This system improves the power factor of welding machine and reduce the harmonics. Simulations results are presented to show the operation of proposed system. Voir les détails
Mots clés : are welding inverter, microcontroller, PWM, IGBT inverter