Khadija Elhamidi | Caddi Ayad (original) (raw)
Papers by Khadija Elhamidi
This paper describes nonlinear dynamics model of an unmanned aerial vehicle of type quadcopter us... more This paper describes nonlinear dynamics model of an unmanned aerial vehicle of type quadcopter using Newton-Euler modeling technique. Usually UAVs systems are unstable and stabilization control plays a very important role. More than half of industrial controllers in use today are PID. Thus, the adjustment aspect of proportional integral derivative (PID) controllers becomes a challenge for research. To tune the controller, two main approaches are proposed. One is linear; the PD gains are fixed in optimal way by using reference model method, while the other is nonlinear and consists of an adaptive hybrid Neural Network based PD (NNPD) control. Hence, simulation results have been performed under MATLAB/Simulink and prove that proposed adaptive hybrid Neural Network based PD (NNPD) controller and reference model method give better results in the term of settling time and precision in Quadrotor UAV application. Keywords— Quadcopter; PD tuning; Neural Network; Reference model. NOMENCLATUR...
Studies in Informatics and Control, 2019
This paper describes nonlinear dynamics model of an unmanned aerial vehicle of type quadcopter us... more This paper describes nonlinear dynamics model of an unmanned aerial vehicle of type quadcopter using Newton-Euler modeling technique. Usually UAVs systems are unstable and stabilization control plays a very important role. More than half of industrial controllers in use today are PID. Thus, the adjustment aspect of proportional integral derivative (PID) controllers becomes a challenge for research. To tune the controller, two main approaches are proposed. One is linear; the PD gains are fixed in optimal way by using reference model method, while the other is nonlinear and consists of an adaptive hybrid Neural Network based PD (NNPD) control. Hence, simulation results have been performed under MATLAB/Simulink and prove that proposed adaptive hybrid Neural Network based PD (NNPD) controller and reference model method give better results in the term of settling time and precision in Quadrotor UAV application. Keywords— Quadcopter; PD tuning; Neural Network; Reference model. NOMENCLATUR...
2015 Third World Conference on Complex Systems (WCCS), 2015
ABSTRACT
Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems, 2020
In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive ... more 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.
Studies in Informatics and Control, 2019
ABSTRACT: The purpose of this research is to design adaptive control methods for addressing the s... more ABSTRACT: The purpose of this research is to design adaptive control methods for addressing the stabilization and trajectory tracking problems in a quadcopter unmanned aerial vehicle (UAV). To accomplish these tasks, a comparative study of the Proportional Integral Derivative (PID) and PD controllers is performed. Intelligent algorithms (IAs) have been used to tune the conventional structure of PID/PD controllers. The proposed hybrid intelligent controllers consist of the neural network PID/PD (NNPID/PD) and the Optimized Fuzzy PID/PD based on the Particle Swarm Optimization (FPID/PDPSO). Adaptive neural networks are deployed to schedule PID/PD gains, the improved back-propagation algorithm is used to update the weights of the neural network. Then, an effective control approach based on adaptive PID Fuzzy logic and Particle Swarm Optimization (PSO) algorithm has been applied. PSO algorithm is introduced to adjust the scaling factors for improving the convergence speed and production rate. Finally, in order to demonstrate the robustness of the proposed control methods, disturbances in the quadcopter system are added. The results so obtained demonstrate the effectiveness of the proposed control strategy.
This paper describes nonlinear dynamics model of an unmanned aerial vehicle of type quadcopter us... more This paper describes nonlinear dynamics model of an unmanned aerial vehicle of type quadcopter using Newton-Euler modeling technique. Usually UAVs systems are unstable and stabilization control plays a very important role. More than half of industrial controllers in use today are PID. Thus, the adjustment aspect of proportional integral derivative (PID) controllers becomes a challenge for research. To tune the controller, two main approaches are proposed. One is linear; the PD gains are fixed in optimal way by using reference model method, while the other is nonlinear and consists of an adaptive hybrid Neural Network based PD (NNPD) control. Hence, simulation results have been performed under MATLAB/Simulink and prove that proposed adaptive hybrid Neural Network based PD (NNPD) controller and reference model method give better results in the term of settling time and precision in Quadrotor UAV application. Keywords— Quadcopter; PD tuning; Neural Network; Reference model. NOMENCLATUR...
Studies in Informatics and Control, 2019
This paper describes nonlinear dynamics model of an unmanned aerial vehicle of type quadcopter us... more This paper describes nonlinear dynamics model of an unmanned aerial vehicle of type quadcopter using Newton-Euler modeling technique. Usually UAVs systems are unstable and stabilization control plays a very important role. More than half of industrial controllers in use today are PID. Thus, the adjustment aspect of proportional integral derivative (PID) controllers becomes a challenge for research. To tune the controller, two main approaches are proposed. One is linear; the PD gains are fixed in optimal way by using reference model method, while the other is nonlinear and consists of an adaptive hybrid Neural Network based PD (NNPD) control. Hence, simulation results have been performed under MATLAB/Simulink and prove that proposed adaptive hybrid Neural Network based PD (NNPD) controller and reference model method give better results in the term of settling time and precision in Quadrotor UAV application. Keywords— Quadcopter; PD tuning; Neural Network; Reference model. NOMENCLATUR...
2015 Third World Conference on Complex Systems (WCCS), 2015
ABSTRACT
Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems, 2020
In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive ... more 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.
Studies in Informatics and Control, 2019
ABSTRACT: The purpose of this research is to design adaptive control methods for addressing the s... more ABSTRACT: The purpose of this research is to design adaptive control methods for addressing the stabilization and trajectory tracking problems in a quadcopter unmanned aerial vehicle (UAV). To accomplish these tasks, a comparative study of the Proportional Integral Derivative (PID) and PD controllers is performed. Intelligent algorithms (IAs) have been used to tune the conventional structure of PID/PD controllers. The proposed hybrid intelligent controllers consist of the neural network PID/PD (NNPID/PD) and the Optimized Fuzzy PID/PD based on the Particle Swarm Optimization (FPID/PDPSO). Adaptive neural networks are deployed to schedule PID/PD gains, the improved back-propagation algorithm is used to update the weights of the neural network. Then, an effective control approach based on adaptive PID Fuzzy logic and Particle Swarm Optimization (PSO) algorithm has been applied. PSO algorithm is introduced to adjust the scaling factors for improving the convergence speed and production rate. Finally, in order to demonstrate the robustness of the proposed control methods, disturbances in the quadcopter system are added. The results so obtained demonstrate the effectiveness of the proposed control strategy.