Institutional repository of university M'Hamed Bougara Boumerdes
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Optimal control design for uncertain aerial manipulator system based on an adaptive approach
No full textIn this paper, an optimal controller has been proposed for an aerial manipulator (AM) consisting of a quadrotor uncertain system with a two-degrees-of-freedom robotic arm. Wherein, the dynamics of this system have been derived based on Gauss's principle. The employment of this principal has permitted the pinpoint of the inner structure of the uncertain system and its possible moves. It has kept the AM in a very precise formation to analyse its dynamics and propose the suitable control. The proposed controller is designed using an adaptive approach of the non-singular terminal sliding mode technique. The main contribution is that the proposed approach guarantees both the good tracking of the desired trajectories in finite time and the chattering effect attenuation without overestimating the switching control gains. The design does not necessitate a priori knowledge of the upper limits of disturbances; the stability of the system has been established through the utilisation of Lyapunov theory. The simulation results have proved the effectiveness and robustness of the proposed optimal nonlinear terminal sliding mode technique for such an uncertain system in comparison to the sliding mode controller
Next-cell prediction with LSTM based on vehicle mobility for 5G mc-IoT slices
No full textNetwork slicing is one 5G network enabler that may be used to enhance the requirements of mission-critical Machine Type Communications (mcMTC) in critical IoT applications. But, in applications with high mobility support, the network slicing will also be influenced by users’ movement, which is necessary to handle the dynamicity of the system, especially for critical slices that require fast and reliable delivery from End to End (E2E). To fulfill the desired service quality (QoS) of critical slices due to their users’ movement. This paper presents mobility awareness for such types of applications through mobility prediction, in which the network can determine which cell the user is in near real-time. Furthermore, the proposed next-cell mobility prediction framework is developed as a multi-classification task, where we exploited Long Short-Term Memory (LSTM) and the collected historical mobility profiles of moving users to allow more accurate short- and long-term predictions of the candidate next-cell. Then, within the scope of high mobility mission-critical use cases, we evaluate the effectiveness of the proposed LSTM classifier in vehicular networks. We have used a real vehicle mobility dataset that is obtained from SUMO deployed in Bejaia, Algeria urban environment. Ultimately, we conducted a set of experiments on the classifier using datasets with various history lengths, and the results have validated the effectiveness of the performed predictions on short-term mobility prediction. Our experiments show that the proposed classifier performs better on longer history datasets. While compared to traditional Machine Learning (ML) algorithms used for classification, the proposed LSTM model outperformed ML methods with the best accurate prediction results
Amélioration de l’efficacité énergétique d’un variateur de vitesse par la méthode "LMC"
No full text58 p. : ill. ; 30 cmLes Machines à induction sont largement utilisées dans les applications industrielles. Toutefois l’entrainement avec ce type de machines souffre de la dégradation de l’efficacité du variateur lors du fonctionnement à vitesse variable et à faibles charges. Pour améliorer l’efficacité du variateur de vitesse par moteur à induction, il faut à tout moment, d’une part, maintenir le découplage entre le couple et le flux rotorique, et d’autre part, chercher la valeur optimale du flux rotorique optimal pour laquelle, les pertes du moteur sont minimales. Autrement, dit maintenir l’efficacité signifie la diminution des coûts d’exploitation. A cet effet, dans le présent travail sont présentés algorithme de minimisation des pertes à base du modèle (Loss model Controller LMC)
Damage assessment in RC columns using the energy dissipation of ultrasonic waves
No full textReinforced concrete (RC) columns, widely used in various types of structures, are prone to significant damage from cyclic loads, such as seismic events or repeated stresses, particularly in older buildings. Accurate damage assessment is crucial for ensuring safety and reliability or determining the need for reinforcement. Traditional evaluation methods often involve time-consuming invasive techniques, whereas non-destructive testing (NDT) methods provide innovative approaches to monitor and assess structural health swiftly and without causing additional damage. This paper investigated the damage assessment in reinforced concrete columns subjected to alternating cyclic loading by calculating the damage index based on ultrasonic energy dissipation. To achieve this, non-destructive tests were conducted alongside mechanical tests. Ultrasonic signals of waves passing through the material, as well as displacements at the top of the columns, were recorded for each loading cycle. The extent of damage, represented by the damage index (DI) value, was estimated using the proposed model, which is based on variations in ultrasonic signal energy. The comparison between the results obtained with the proposed model and those from other authors demonstrates the effectiveness and validity of the proposed model based on ultrasonic signal energy for damage index evaluation. Additionally, this approach facilitates the assessment of structural conditions after an earthquake and enhances structural health monitoring (SHM)
Chemical compositions and insectisidal activity of essential oils of three plants Artemisia SP: Artemisia herba-alba, Artemisia absinthium and Artemisia Pontica (Morocco)
No full textMany researchers and manufacturers around the world have shown interest in essential oils and their components. Artemisia absinthium L., commonly known as wormwood, is a medicinal and aromatic bitter herb frequently used in traditional medicine since ancient times. The objective of this research was to study the chemical composition and evaluate the insecticidal activities of A. absinthium essential oil (AEO) against Tenebrio molitor L., mealworm battle. We explored its effectiveness through several means, including direct contact, fumigation and repulsion of insects. Phytochemical analysis was performed using GC-MS technique. The main components identified in the essential oil were camphor (32.34%), chamazulene (13.92%), and terpinen-4-ol (10.18%). These results highlight that Algerian A. absinthium plants are valuable resources due to their bioactive compounds. In particular, the results obtained in our investigation suggest that the essential oil obtained from A. absinthium has the potential to be used as a natural bioinsecticide
Accurate asymmetrical minor loops modeling with the modified arctangent hysteresis model
No full textIn the area of automotive propulsion, electric motors experience core losses due to the presence of higher harmonic frequencies, particularly those arising from pulse width modulation switching. Consequently, it is required to establish a practical model capable of characterizing the hysteresis loops in electrical steel when subjected to harmonic excitations. Nonetheless, existing dynamic models may be inappropriate for real-world applications due to their inherent complexity and their tendency to neglect the influence of minor loops arising from harmonics. In the present work, the minor and major hysteresis curves of non-oriented silicon steel sheets are measured under variable frequency excitation containing harmonic components. To reproduce the asymmetrical minor hysteresis loops, a modified arctangent model is employed. The evaluation of the accuracy of this hysteresis model is conducted by comparing the fitted results of static and dynamic hysteresis loops against experimental data, both with and without harmonic excitation
Étude de la résistance et de la rupture du réservoir de stockage des hydrocarbures
No full text63 p. : ill. ; 30 cmDans ce mémoire, nous aborderons les sujets suivants : une introduction sur les réservoirs de stockage, leurs différents types et leurs particularités. Les problèmes de dysfonctionnement et types des de défaillance dans les réservoirs de stockage. Le calcul de la résistance mécanique de notre réservoir. La simulation numérique pour évaluer la résistance du réservoir de stockage en présence d’une fissure dans sa paroi latérale qui peut se produire dans la direction axiale de la première couche de la coque, où les contraintes circonférentielles les plus importantes se manifestent. Une analyse par le diagramme d’analyse des défaillances « FAD » a été effectuée dans le but d’obtenir les dimensions critiques de la fissure. Ainsi que le calcul par la méthode des éléments finis, par simulation par « ANSYS »
Open-Switches fault diagnosis and fault tolerant direct torque control of voltage source inverter fed induction motor
No full textFault diagnosis and fault tolerance are considered essential features in critical industrial applications in order to maintain the necessary levels of availability and dependability. The components that are most frequently affected by failures in closed-loop controlled power converters are semiconductors and sensors. This work focuses on fault tolerant direct torque control (DTC) of induction motor drive systems under inverter open-switch failure. This system detects and isolates the fault, and then ensures the system’s operation under the new conditions. The solution was accomplished using a new reduced switch converter. This system modifies the DTC switching table using available stator voltage vectors in two-phase mode with a Four Switch Three Phase Inverter (FSTPI) topology to maintain the performance of the motor as in the Six Switch Three Phase Inverter (SSTPI) mode. The effectiveness of the fault detection method and fault tolerant control algorithm have been investigated using MATLAB/Simulink software
Automatic fault tracking from 3D seismic data using the 2D Continuous Wavelet Transform combined with a Convolutional Neural Network
No full textThe aim of this work is to propose a new technique for automatic fault tracking from 3D seismic data using the 2D Continuous Wavelet Transform (CWT) method combined with artificial intelligence. Time slices of the variance attribute, derived from the 3D seismic data and chosen by the user, are analysed using the 2D CWT with the 2D Mexican Hat as an analysing wavelet, and the maxima of the modulus of the 2D CWT are mapped for the full range of scales. The ensemble of mapped maxima for the set of time slices is filtered using a Convolutional Neural Network machine. Machine training is performed with a supervised mode using the manually tracked faults as a desired output. Application to real data shows the efficiency and robustness of the proposed method, which can greatly help seismic interpreters in avoiding manual fault tracking, a difficult and time-consuming task
Modeling wax disappearance temperature using robust white-box machine learning
No full textWax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly-introduced correlations provided excellent statistical metrics (R2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues