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This paper introduces a nonlinear adaptive controller of unknown nonlinear dynamical systems based on the approximate models using a multi-layer perceptron neural network. The proposal of this study is to employ the structure of the Multi-Layer Perceptron (MLP) model into the NARMA-L2 structure in order to construct a hybrid neural structure that can be used as an identifier model and a nonlinear controller for the MIMO nonlinear systems. The big advantage of the proposed control system is that it doesn’t require previous knowledge of the model. Our ultimate goal is to determine the control input using only the values of the input and output. The developed NARMA-L2 neural network model is tuned for its weights employing the backpropagation optimizer algorithm. Nonlinear autoregressive-moving average-L2 (NARMA-L2) neural network controller, based on the inputs and outputs from the nonlinear model, is designed to perform control action on the nonlinear for the attitude control of unmanned aerial vehicles (UAVs) model. Once the system has been modeled efficiently and accurately, the proposed controller is designed by rearranging the generalized submodels. The controller performance is evaluated by simulation conducted on a quadcopter MIMO system, which is characterized by a nonlinear and dynamic behavior. The obtained results show that the NARMA-L2-based neural network achieved good performances in modeling and control.
K. El Hamidi; M. Mjahed; A. El Kari; H. Ayad; N. El Gmili. Design of Hybrid Neural Controller for Nonlinear MIMO System Based on NARMA-L2 Model. IETE Journal of Research 2021, 1 -14.
AMA StyleK. El Hamidi, M. Mjahed, A. El Kari, H. Ayad, N. El Gmili. Design of Hybrid Neural Controller for Nonlinear MIMO System Based on NARMA-L2 Model. IETE Journal of Research. 2021; ():1-14.
Chicago/Turabian StyleK. El Hamidi; M. Mjahed; A. El Kari; H. Ayad; N. El Gmili. 2021. "Design of Hybrid Neural Controller for Nonlinear MIMO System Based on NARMA-L2 Model." IETE Journal of Research , no. : 1-14.
Ahmed Youssef Ouadine; Mostafa Mjahed; Hassan Ayad; Abdeljalil El Kari. UAV Quadrotor Fault Detection and Isolation Using Artificial Neural Network and Hammerstein-Wiener Model. Studies in Informatics and Control 2020, 29, 317 -328.
AMA StyleAhmed Youssef Ouadine, Mostafa Mjahed, Hassan Ayad, Abdeljalil El Kari. UAV Quadrotor Fault Detection and Isolation Using Artificial Neural Network and Hammerstein-Wiener Model. Studies in Informatics and Control. 2020; 29 (3):317-328.
Chicago/Turabian StyleAhmed Youssef Ouadine; Mostafa Mjahed; Hassan Ayad; Abdeljalil El Kari. 2020. "UAV Quadrotor Fault Detection and Isolation Using Artificial Neural Network and Hammerstein-Wiener Model." Studies in Informatics and Control 29, no. 3: 317-328.
Path planning is one of the research axes in mobile robotics, it allows us to plan a path linked, the robot's starting point and the goal to guarantee autonomous navigation for the mobile robot. This work presents a quantitative methodology to compare the Particle Swarm Optimization algorithm and Fuzzy Logic, applied to solve the path-planning problem, taking into account their basic concepts and results obtained.
Ahmed Oultiligh; Hassan Ayad; Abdeljalil Elkari; Mostafa Mjahed. Path Planning Using Particle Swarm Optimization and Fuzzy Logic. Advances in Intelligent Systems and Computing 2020, 239 -251.
AMA StyleAhmed Oultiligh, Hassan Ayad, Abdeljalil Elkari, Mostafa Mjahed. Path Planning Using Particle Swarm Optimization and Fuzzy Logic. Advances in Intelligent Systems and Computing. 2020; ():239-251.
Chicago/Turabian StyleAhmed Oultiligh; Hassan Ayad; Abdeljalil Elkari; Mostafa Mjahed. 2020. "Path Planning Using Particle Swarm Optimization and Fuzzy Logic." Advances in Intelligent Systems and Computing , no. : 239-251.
In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.
Khadija El Hamidi; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad. Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems. Modelling and Simulation in Engineering 2020, 2020, 1 -13.
AMA StyleKhadija El Hamidi, Mostafa Mjahed, Abdeljalil El Kari, Hassan Ayad. Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems. Modelling and Simulation in Engineering. 2020; 2020 ():1-13.
Chicago/Turabian StyleKhadija El Hamidi; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad. 2020. "Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems." Modelling and Simulation in Engineering 2020, no. : 1-13.
Photovoltaic (PV) energy represents one of the most important renewable energies, but its disadvantage resides in its maximum power point, which varies according to meteorological changes that make the efficiency low. Intelligent techniques, using the maximum power point tracking (MPPT) method, can achieve an efficient real‐time tracking of this point in order to ensure optimal functioning of the system. The output power of the PV system is removed from solar irradiation and cell temperature of the PV panel type SOLON 55W. Therefore, it is essential to harvest the generated power of the PV system and optimally exploit the collected solar energy. For this objective, this work treats on a new artificial neural network‐particle swarm optimization approach (ANN‐PSO). The ANN is used to predict the solar irradiation level and cell temperature followed by PSO to optimize the power generation and optimally track the solar power of the PV panel type SOLON 55W based on various operation conditions under changes in environmental conditions. The simulation results of the proposed approach give a minimum error with a relevant efficiency, that is, the power provided by ANN‐PSO approach is optimal and closer to the PV power. Consequently, this novel approach ANN‐PSO shows its major capability to extract the optimal power with excellent efficiency up of 97%. For this objective, this work treats a new hybrid ANN‐PSO approach.
Aouatif Ibnelouad; Abdeljalil El Kari; Hassan Ayad; Mostafa Mjahed. Improved cooperative artificial neural network ‐ particle swarm optimization approach for solar photovoltaic systems using maximum power point tracking. International Transactions on Electrical Energy Systems 2020, 30, 1 .
AMA StyleAouatif Ibnelouad, Abdeljalil El Kari, Hassan Ayad, Mostafa Mjahed. Improved cooperative artificial neural network ‐ particle swarm optimization approach for solar photovoltaic systems using maximum power point tracking. International Transactions on Electrical Energy Systems. 2020; 30 (8):1.
Chicago/Turabian StyleAouatif Ibnelouad; Abdeljalil El Kari; Hassan Ayad; Mostafa Mjahed. 2020. "Improved cooperative artificial neural network ‐ particle swarm optimization approach for solar photovoltaic systems using maximum power point tracking." International Transactions on Electrical Energy Systems 30, no. 8: 1.
Khadija El Hamidi; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad. Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking. Studies in Informatics and Control 2019, 28, 401 -412.
AMA StyleKhadija El Hamidi, Mostafa Mjahed, Abdeljalil El Kari, Hassan Ayad. Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking. Studies in Informatics and Control. 2019; 28 (4):401-412.
Chicago/Turabian StyleKhadija El Hamidi; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad. 2019. "Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking." Studies in Informatics and Control 28, no. 4: 401-412.
Nada El Gmili; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad. Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking. Automatika 2019, 61, 189 -206.
AMA StyleNada El Gmili, Mostafa Mjahed, Abdeljalil El Kari, Hassan Ayad. Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking. Automatika. 2019; 61 (2):189-206.
Chicago/Turabian StyleNada El Gmili; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad. 2019. "Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking." Automatika 61, no. 2: 189-206.
This paper explores the model parameters estimation of a quadrotor UAV by exploiting the cooperative particle swarm optimization-cuckoo search (PSO-CS). The PSO-CS regulates the convergence velocity benefiting from the capabilities of social thinking and local search in PSO and CS. To evaluate the efficiency of the proposed methods, it is regarded as important to apply these approaches for identifying the autonomous complex and nonlinear dynamics of the quadrotor. After defining the quadrotor dynamic modelling using Newton–Euler formalism, the quadrotor model’s parameters are extracted by using intelligent PSO, CS, PSO-CS, and the statistical least squares (LS) methods. Finally, simulation results prove that PSO and PSO-CS are more efficient in optimal tuning of parameters values for the quadrotor identification.
Nada El Gmili; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad. Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach. Computational Intelligence and Neuroscience 2019, 2019, 1 -10.
AMA StyleNada El Gmili, Mostafa Mjahed, Abdeljalil El Kari, Hassan Ayad. Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach. Computational Intelligence and Neuroscience. 2019; 2019 ():1-10.
Chicago/Turabian StyleNada El Gmili; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad. 2019. "Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach." Computational Intelligence and Neuroscience 2019, no. : 1-10.
This paper deals with the trajectory tracking problem for a quadrotor unmanned aerial vehicle (UAV). For this purpose, two control strategies are proposed. First, a flight controller with a hierarchical structure is designed, whereby the complete closed-loop system is divided into two blocks. The system has an inner block for attitude control and an outer block for position stabilization, for a total of six proportional-derivative/proportional-integral-derivative (PD/PID) controllers. The second new trajectory tracking strategy is based on attitude stabilization. In addition to a direct stabilization of yaw and altitude, the x and y positions are stabilized by choosing an appropriate control of roll and pitch angles. The relations between positions (x, y) and rotations (roll, pitch) are derived from the natural flight of the quadcopter. In this second approach, with only four controllers, the quadrotor UAV is able to follow any trajectory. In both approaches, the PD/PID controllers are synthesized using the genetic algorithm method, and compared with those obtained by the reference model method. Furthermore, a comparison between PD and PID controller performance is performed. Thereafter, the robustness of the proposed controllers is tested for trajectory tracking in a disturbed environment. Simulation results demonstrate that for the two approaches, PD controllers show a better behavior with respect to quadcopter stabilization than in trajectory tracking under different conditions.
Imane Siti; Mostafa Mjahed; Hassan Ayad; Abdeljalil El Kari. New Trajectory Tracking Approach for a Quadcopter Using Genetic Algorithm and Reference Model Methods. Applied Sciences 2019, 9, 1780 .
AMA StyleImane Siti, Mostafa Mjahed, Hassan Ayad, Abdeljalil El Kari. New Trajectory Tracking Approach for a Quadcopter Using Genetic Algorithm and Reference Model Methods. Applied Sciences. 2019; 9 (9):1780.
Chicago/Turabian StyleImane Siti; Mostafa Mjahed; Hassan Ayad; Abdeljalil El Kari. 2019. "New Trajectory Tracking Approach for a Quadcopter Using Genetic Algorithm and Reference Model Methods." Applied Sciences 9, no. 9: 1780.
This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) byexploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search(CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposedPSO-CS algorithm combines the ability of social thinking in PSO with the local search capability inCS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotordynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral,and Derivative) controllers are optimized by using the intelligent proposed approaches and theclassical method of Reference Model (RM) for quadrotor full control. Finally, simulation resultsprove that PSO and PSO-CS are more efficient in tuning of optimal parameters for the quadrotorcontrol. Indeed, the ability of PSO and PSO-CS to track the imposed trajectories is well seen from3D path tracking simulations and even in presence of wind disturbances.
Nada El Gmili; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad; El Gmili; El Kari; Ayad. Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking. Applied Sciences 2019, 9, 1719 .
AMA StyleNada El Gmili, Mostafa Mjahed, Abdeljalil El Kari, Hassan Ayad, El Gmili, El Kari, Ayad. Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking. Applied Sciences. 2019; 9 (8):1719.
Chicago/Turabian StyleNada El Gmili; Mostafa Mjahed; Abdeljalil El Kari; Hassan Ayad; El Gmili; El Kari; Ayad. 2019. "Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking." Applied Sciences 9, no. 8: 1719.
In this paper, we implemented a diagnostic system for vibration faults that occur on the PUMA helicopter gearbox. We used an approach based on the joint use of the Order Tracking signal analysis and the Genetic Algorithm. To achieve this goal, we first collected a database of vibration signals measured during periodic inspections. The available vibration signals are acquired under a time-varying operating conditions. Therefore, we used the Order Tracking method, which is more accurate in extracting faults features. This technique was performed by resampling the vibration data and then applying the Short Time Fourier Transform. To enable efficient and continuous monitoring of gearbox vibration faults from features, we used Genetic Algorithm to build a rules-based diagnostic model. Genetic operators have been adapted to the specificity of the problem to optimize the parameters of this model. This approach is successfully applied to the diagnosis of vibration defects of helicopter gearboxes. The results have been validated effectively with test data. The diagnostic model can therefore be implemented on helicopter computers to detect faults in flight or on the ground. This approach has been used for the first time in the field of helicopter gearbox vibration fault diagnosis.
Ahmed Youssef Ouadine; Mostafa Mjahed; Hassan Ayad; Abdeljalil El Kari. Helicopter gearbox vibration fault classification using order tracking method and genetic algorithm. Automatika 2019, 60, 68 -78.
AMA StyleAhmed Youssef Ouadine, Mostafa Mjahed, Hassan Ayad, Abdeljalil El Kari. Helicopter gearbox vibration fault classification using order tracking method and genetic algorithm. Automatika. 2019; 60 (1):68-78.
Chicago/Turabian StyleAhmed Youssef Ouadine; Mostafa Mjahed; Hassan Ayad; Abdeljalil El Kari. 2019. "Helicopter gearbox vibration fault classification using order tracking method and genetic algorithm." Automatika 60, no. 1: 68-78.
Monitoring and diagnosis of rotating machines has become an effective and indispensable tool for the efficient and timely detection of defects, avoiding then incidents that can have serious economic and human consequences. Bearings are the most sensitive parts of these machines; this is why special attention must be paid to its monitoring. This paper presents a methodology for diagnosing an aircraft air compressor bearing using neural networks that have been optimized by genetic algorithms. We used in our study a database of vibratory signals that were recorded on a test bench from bearings with different defects. The faults features are extracted from these noisy signals using the estimate of the spectral density. The diagnostic capacity of obtained model has been demonstrated by a comparative study with two other automatic classifiers, which are discriminant analysis and neural networks whose training has been done with the Back-Propagation algorithm. This approach has the advantage of simultaneously ensuring the optimal structure of the neural network and accomplishing its learning. The importance of this study is the construction of a diagnostic tool that is characterized by efficiency, speed of decision making and ease of implementation not only on the computers on the ground, but also on the mounted calculators on aircraft.
Ahmed Youssef Ouadine; Mostafa Mjahed; Hassan Ayad; Abdeljalil El Kari. Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches. Applied Sciences 2018, 8, 2243 .
AMA StyleAhmed Youssef Ouadine, Mostafa Mjahed, Hassan Ayad, Abdeljalil El Kari. Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches. Applied Sciences. 2018; 8 (11):2243.
Chicago/Turabian StyleAhmed Youssef Ouadine; Mostafa Mjahed; Hassan Ayad; Abdeljalil El Kari. 2018. "Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches." Applied Sciences 8, no. 11: 2243.
In this study, we develop a rigorous tracking control approach for quadrotor unmanned aerial vehicles (UAVs) with unknown dynamics, unknown physical parameters, and subject to unknown and unpredictable disturbances. In order to better estimate the unknown functions, seven interval type-2-adaptive fuzzy systems (IT2-AFSs) and five adaptive systems are designed. Then, a new IT2 adaptive fuzzy reaching sliding mode system (IT2-AFRSMS) which generates an optimal smooth adaptive fuzzy reaching sliding mode control law (AFRSMCL) using IT2-AFSs is introduced. The AFRSMCL is designed a way that ensures that its gains are efficiently estimated. Thus, the global proposed control law can effectively achieve the predetermined performances of the tracking control while simultaneously avoiding the chattering phenomenon, despite the approximation errors and all disturbances acting on the quadrotor dynamics. The adaptation laws are designed by utilizing the stability analysis of Lyapunov. A simulation example is used to validate the robustness and effectiveness of the proposed method of control. The obtained results confirm the results of the mathematical analysis in guaranteeing the tracking convergence and stability of the closed loop dynamics despite the unknown dynamics, unknown disturbances, and unknown physical parameters of the controlled system.
Nabil Nafia; Abdeljalil El Kari; Hassan Ayad; Mostafa Mjahed. Robust Full Tracking Control Design of Disturbed Quadrotor UAVs with Unknown Dynamics. Aerospace 2018, 5, 115 .
AMA StyleNabil Nafia, Abdeljalil El Kari, Hassan Ayad, Mostafa Mjahed. Robust Full Tracking Control Design of Disturbed Quadrotor UAVs with Unknown Dynamics. Aerospace. 2018; 5 (4):115.
Chicago/Turabian StyleNabil Nafia; Abdeljalil El Kari; Hassan Ayad; Mostafa Mjahed. 2018. "Robust Full Tracking Control Design of Disturbed Quadrotor UAVs with Unknown Dynamics." Aerospace 5, no. 4: 115.
This article discusses the modeling and control of a wind energy system, the turbine based on the synchronous generator with permanent magnet. At first, the modeling of the various elements of a chain wind turbine connected to the network through the equations that govern them will be presented. After, the strategy by fuzzy logic and by neural networks will be proposed in order to control and maximize the power retrieved regardless of the speed of the wind. At last, a simulation of the chain of a wind turbine in closed loop and with the two command methods of MPPT. They are presented through the MATLAB/Simulink environment as well of a comparison of the advantages and disadvantages of each.
Baddou Abdelmjid; El Kari Abdeljalil; Ayad Hassan; Mjahed Mostafa. Intelligent Methods for the Maximization of the Energy of Wind Systems with Synchronous Generators Permanent Magnet. Lecture Notes in Electrical Engineering 2018, 587 -596.
AMA StyleBaddou Abdelmjid, El Kari Abdeljalil, Ayad Hassan, Mjahed Mostafa. Intelligent Methods for the Maximization of the Energy of Wind Systems with Synchronous Generators Permanent Magnet. Lecture Notes in Electrical Engineering. 2018; ():587-596.
Chicago/Turabian StyleBaddou Abdelmjid; El Kari Abdeljalil; Ayad Hassan; Mjahed Mostafa. 2018. "Intelligent Methods for the Maximization of the Energy of Wind Systems with Synchronous Generators Permanent Magnet." Lecture Notes in Electrical Engineering , no. : 587-596.
This paper develops a new robust tracking control design for n-link robot manipulators with dynamic uncertainties, and unknown disturbances. The procedure is conducted by designing two adaptive interval type-2 fuzzy logic systems (AIT2-FLSs) to better approximate the parametric uncertainties on the system nominal. Then, in order to achieve the best tracking control performance and to enhance the system robustness against approximation errors and unknown disturbances, a new control algorithm, which uses a new synthesized AIT2 fuzzy sliding mode control (AIT2-FSMC) law, has been proposed. To deal with the chattering phenomenon without deteriorating the system robustness, the AIT2-FSMC has been designed so as to generate three adaptive control laws that provide the optimal gains value of the global control law. The adaptation laws have been designed in the sense of the Lyapunov stability theorem. Mathematical proof shows that the closed loop control system is globally asymptotically stable. Finally, a 2-link robot manipulator is used as case study to illustrate the effectiveness of the proposed control approach.
Nabil Nafia; Abdeljalil El Kari; Hassan Ayad; Mostafa Mjahed. Robust Interval Type-2 Fuzzy Sliding Mode Control Design for Robot Manipulators. Robotics 2018, 7, 40 .
AMA StyleNabil Nafia, Abdeljalil El Kari, Hassan Ayad, Mostafa Mjahed. Robust Interval Type-2 Fuzzy Sliding Mode Control Design for Robot Manipulators. Robotics. 2018; 7 (3):40.
Chicago/Turabian StyleNabil Nafia; Abdeljalil El Kari; Hassan Ayad; Mostafa Mjahed. 2018. "Robust Interval Type-2 Fuzzy Sliding Mode Control Design for Robot Manipulators." Robotics 7, no. 3: 40.
In this paper, in order to achieve the best tracking control of a class of multi-input multi-output (MIMO) nonlinear systems with unknown dynamics and unknown disturbances, a new robust adaptive interval type-2 fuzzy sliding mode control law (AIT2-FSMCL) has been proposed. Based on developing interval type-2 fuzzy local models for some operating points of the controlled system, an interval type-2 fuzzy logic system (IT2-FLS) has been designed to better estimate the unknown nonlinear dynamics of the studied system. Then, to enhance the tracking control performance and ensure the system robustness in the presence of approximation errors, parameter variations, un-modelled dynamics and external disturbances, a new AIT2-fuzzy sliding mode system (AIT2-FSMS), has been introduced. In order to avoid the chattering phenomenon while keeping the system performance, the AIT2-FSMS uses three AIT2-fuzzy logic systems (AIT2-FLSs) to estimate the optimal gains of the AIT2-FSMCL. The adaptation laws have been derived using the Lyapunov stability approach. The mathematical proof shows that the closed-loop system with the proposed control approach is globally asymptotically stable. Finally, the proposed design method is applied to a two-link robot arm to validate the effectiveness of the proposed control approach.
Nabil Nafia; Abdeljalil El Kari; Hassan Ayad; Mostafa Mjahed. A robust type-2 fuzzy sliding mode controller for disturbed MIMO nonlinear systems with unknown dynamics. Automatika 2018, 59, 194 -207.
AMA StyleNabil Nafia, Abdeljalil El Kari, Hassan Ayad, Mostafa Mjahed. A robust type-2 fuzzy sliding mode controller for disturbed MIMO nonlinear systems with unknown dynamics. Automatika. 2018; 59 (2):194-207.
Chicago/Turabian StyleNabil Nafia; Abdeljalil El Kari; Hassan Ayad; Mostafa Mjahed. 2018. "A robust type-2 fuzzy sliding mode controller for disturbed MIMO nonlinear systems with unknown dynamics." Automatika 59, no. 2: 194-207.
This research aims to design the tuning of a PID controller for a non linear system, using PSO algorithm as an intelligent procedure. Simulation result demonstrates that our proposed method is more efficient and robust.
El Gmili Nada; Mjahed Mostafa; El Kari Abdeljalil; Ayad Hassan. Stabilization of a non-linear system by using Particle Swarm Optimization (PSO) method. 2015 Third World Conference on Complex Systems (WCCS) 2015, 1 -5.
AMA StyleEl Gmili Nada, Mjahed Mostafa, El Kari Abdeljalil, Ayad Hassan. Stabilization of a non-linear system by using Particle Swarm Optimization (PSO) method. 2015 Third World Conference on Complex Systems (WCCS). 2015; ():1-5.
Chicago/Turabian StyleEl Gmili Nada; Mjahed Mostafa; El Kari Abdeljalil; Ayad Hassan. 2015. "Stabilization of a non-linear system by using Particle Swarm Optimization (PSO) method." 2015 Third World Conference on Complex Systems (WCCS) , no. : 1-5.
This paper concern the control of a nonlinear system using two different methods, reference model and genetic algorithm. The aim is to synthesize a PD/PID to correct the behavior of a quadcopter. The dynamic model of this system is developed and corrected in the first step by a classical PD, in second step by a genetic based PD/PID controller. The simulation results are illustrated.
Siti Imane; Mjahed Mostafa; Ayad Hassan; El Kari Abdeljalil. Control of a quadcopter using reference model and genetic algorithm methods. 2015 Third World Conference on Complex Systems (WCCS) 2015, 1 -6.
AMA StyleSiti Imane, Mjahed Mostafa, Ayad Hassan, El Kari Abdeljalil. Control of a quadcopter using reference model and genetic algorithm methods. 2015 Third World Conference on Complex Systems (WCCS). 2015; ():1-6.
Chicago/Turabian StyleSiti Imane; Mjahed Mostafa; Ayad Hassan; El Kari Abdeljalil. 2015. "Control of a quadcopter using reference model and genetic algorithm methods." 2015 Third World Conference on Complex Systems (WCCS) , no. : 1-6.
This article presents the modeling and control of an unmanned aerial vehicle (a Quadcopter). Its modeling will be described by using Newton Euler equations. In order to control the attitude and altitude of the Quadcopter, two approaches have been proposed for adjusting the parameters of a PID controller. Firstly, the PD controller gains are fixed in an optimal way by using reference model method. In the second approach these parameters are adapted online based on fuzzy logic. Matlab/Simulink has been used to test and compare the performance of the controllers obtained. This study showed that the reference model method and fuzzy techniques can properly control the system.
El Hamidi Khadija; El Kari Abdeljalil; Mjahed Mostafa; Ayad Hassan. Adapting parameters for flight control of a quadcopter using reference model and fuzzy logic. 2015 Third World Conference on Complex Systems (WCCS) 2015, 1 -6.
AMA StyleEl Hamidi Khadija, El Kari Abdeljalil, Mjahed Mostafa, Ayad Hassan. Adapting parameters for flight control of a quadcopter using reference model and fuzzy logic. 2015 Third World Conference on Complex Systems (WCCS). 2015; ():1-6.
Chicago/Turabian StyleEl Hamidi Khadija; El Kari Abdeljalil; Mjahed Mostafa; Ayad Hassan. 2015. "Adapting parameters for flight control of a quadcopter using reference model and fuzzy logic." 2015 Third World Conference on Complex Systems (WCCS) , no. : 1-6.
Genetic Algorithms are used to separate the signal from the background in the Standard Model Higgs boson search at LHC. Based on Monte Carlo events, using PYTHIA 6.1 and produced at LHC energies, two approaches are investigated. First discriminant function parameters and neural network connection weights are optimized. In a multidimensionnal search approach, hyperplanes and hypersurfaces are computed. In both cases, the performances are improved and the results compare favorably with other multivariate analysis.
Mostafa Mjahed. Search for the Higgs boson at LHC by using Genetic Algorithms. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2006, 559, 172 -176.
AMA StyleMostafa Mjahed. Search for the Higgs boson at LHC by using Genetic Algorithms. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2006; 559 (1):172-176.
Chicago/Turabian StyleMostafa Mjahed. 2006. "Search for the Higgs boson at LHC by using Genetic Algorithms." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 559, no. 1: 172-176.