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Design and Implementaion of a Customized Autopilotتصميم وتنفيذ طيار آلي محدود التكلفة ,Design and Implementaion of a Customized...
تصميم وتنفيذ طيار آلي محدود التكلفة ,Design and Implementaion of a Customized...Ahmed Momtaz Hosny, PhD
Inferring the Optimum UAV's Trajectories Configuration Over Definite Intruder Paths with Multiple Random Starting Points Via Genetic Algorithm - Taking into consideration a set of synchronized UAV's - (Provided with the relevant Matlab Code).
Inferring the Optimum UAV's Trajectories Configuration Over Definite Intruder...
Inferring the Optimum UAV's Trajectories Configuration Over Definite Intruder...Ahmed Momtaz Hosny, PhD
This work investigates the performance on the aircraft control system during air refuel purposes of the Linear Quadratic Regulator (LQR) control alone, and the integration between fuzzy control and LQR. LQR is modern linear control that is suitable for multivariable state feedback and is known to yield good performance for linear systems or for nonlinear systems where the nonlinear aspects are presented. The fuzzy control is known to have the ability to deal with nonlinearities without having to use advanced mathematics. The LQR integrated fuzzy control (LQRIFC) simultaneously makes use of the good performance of the LQR in the region close to switching curve, and the effectiveness of the fuzzy control in region away from switching curve. A new analysis of the fuzzy system behavior presented helps to make possible precise integration of LQR features into the fuzzy control. The LQRIFC is verified by simulation to suppress the uncertain instability more effectively than the LQR alone besides minimizing the time of the mission proposed. The aerial refueling process has been divided into 3 basic stages (initial stability stage, tracking stage, alignment stage) to meet all the process requirements and constraints with minimizing the whole mission time period. GA has been used to tune the fuzzy logic controller parameters and some PID’s. The control strategy was applied to both aerial refueling methods. And the MATLAB simulation results show the validity and the efficiency of using the proposed control approach. Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...
Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...Ahmed Momtaz Hosny, PhD
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Design and Implementaion of a Customized Autopilotتصميم وتنفيذ طيار آلي محدود التكلفة ,Design and Implementaion of a Customized...
تصميم وتنفيذ طيار آلي محدود التكلفة ,Design and Implementaion of a Customized...Ahmed Momtaz Hosny, PhD
Inferring the Optimum UAV's Trajectories Configuration Over Definite Intruder Paths with Multiple Random Starting Points Via Genetic Algorithm - Taking into consideration a set of synchronized UAV's - (Provided with the relevant Matlab Code).
Inferring the Optimum UAV's Trajectories Configuration Over Definite Intruder...
Inferring the Optimum UAV's Trajectories Configuration Over Definite Intruder...Ahmed Momtaz Hosny, PhD
This work investigates the performance on the aircraft control system during air refuel purposes of the Linear Quadratic Regulator (LQR) control alone, and the integration between fuzzy control and LQR. LQR is modern linear control that is suitable for multivariable state feedback and is known to yield good performance for linear systems or for nonlinear systems where the nonlinear aspects are presented. The fuzzy control is known to have the ability to deal with nonlinearities without having to use advanced mathematics. The LQR integrated fuzzy control (LQRIFC) simultaneously makes use of the good performance of the LQR in the region close to switching curve, and the effectiveness of the fuzzy control in region away from switching curve. A new analysis of the fuzzy system behavior presented helps to make possible precise integration of LQR features into the fuzzy control. The LQRIFC is verified by simulation to suppress the uncertain instability more effectively than the LQR alone besides minimizing the time of the mission proposed. The aerial refueling process has been divided into 3 basic stages (initial stability stage, tracking stage, alignment stage) to meet all the process requirements and constraints with minimizing the whole mission time period. GA has been used to tune the fuzzy logic controller parameters and some PID’s. The control strategy was applied to both aerial refueling methods. And the MATLAB simulation results show the validity and the efficiency of using the proposed control approach. Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...
Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...Ahmed Momtaz Hosny, PhD
This work investigates the performance on the aircraft control system during air refuel purposes of the Linear Quadratic Regulator (LQR) control alone, and the integration between fuzzy control and LQR. LQR is modern linear control that is suitable for multivariable state feedback and is known to yield good performance for linear systems or for nonlinear systems where the nonlinear aspects are presented. The fuzzy control is known to have the ability to deal with nonlinearities without having to use advanced mathematics. The LQR integrated fuzzy control (LQRIFC) simultaneously makes use of the good performance of the LQR in the region close to switching curve, and the effectiveness of the fuzzy control in region away from switching curve. A new analysis of the fuzzy system behavior presented helps to make possible precise integration of LQR features into the fuzzy control. The LQRIFC is verified by simulation to suppress the uncertain instability more effectively than the LQR alone besides minimizing the time of the mission proposed. The aerial refueling process has been divided into 3 basic stages (initial stability stage, tracking stage, alignment stage) to meet all the process requirements and constraints with minimizing the whole mission time period. GA has been used to tune the fuzzy logic controller parameters and some PID’s. The control strategy was applied to both aerial refueling methods. And the MATLAB simulation results show the validity and the efficiency of using the proposed control approach.
Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...
Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...Ahmed Momtaz Hosny, PhD
ABSTRACT
In recent years, there has been an increasing interest in the fusion of neural networks and fuzzy logic specially in missile control problems. A technique for the preliminary design of a control system is presented using a neurofuzzy approach for a highly nonlinear MIMO 5_DOF AIM 9R model. The model reflects cross coupling effects between the longitudinal and lateral motions. Two neural network controllers are used for the low level control of each motion separately. The control effort of these networks is then blended by a fuzzy logic controller to obtain the overall control action.The fuzzy controller which is a Mamdani type inference system has 25 rule base designed to cope with model uncertainties specially in cross coupling between lateral and longitudinal motions. A computer simulation is performed to compare between various control techniques. The result showed the effectiveness of the hybrid system compared to other control strategies where fuzzy systems or neural networks are used separately.
DEVELOPMENT OF NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MISS...
DEVELOPMENT OF NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MISS...Ahmed Momtaz Hosny, PhD
In this paper, system identifications of an unmanned aerial vehicle (UAV) based on inferred state space and multiple neural networks were presented. In this work an optimization approach was used to conclude an inferred state space and the multiple neural networks system identifications based on the genetic algorithms separately. The UAV is a multi-input multi-output (MIMO) nonlinear system. Models for such MIMO system are expected to be adaptive to dynamic behavior and robust to environment.A Comparison Study between Inferred State-Space and Neural Network Based Syst...
A Comparison Study between Inferred State-Space and Neural Network Based Syst...Ahmed Momtaz Hosny, PhD
The objective of this paper is to develop a customized autopilot system that enables a helicopter model to carry out an autonomous flight using on-board microcontroller. The main goal of this project is to provide a comprehensive controller design methodology, Modeling, simulation, guidance and verification for an unmanned helicopter model. The autopilot system was designed to demonstrate autonomous maneuvers such as flying over the planned waypoints with constant forward speed.Development of a Customized Autopilot for Unmanned Helicopter Model Using Gen...
Development of a Customized Autopilot for Unmanned Helicopter Model Using Gen...Ahmed Momtaz Hosny, PhD
Abstract:
In this paper, system identifications of an unmanned aerial vehicle (UAV) based on inferred state space and multiple neural networks were presented. In this work an optimization approach was used to conclude an inferred state space and the multiple neural networks system identifications based on the genetic algorithms separately.
The presented work is focusing on an inferred state space based system identification which is a new approach seldom used, but it is also easier and more stable compared with the multi-network based system identification during the modeling of dynamic behavior of nonlinear systems.
A Comparison Study between Inferred State-Space and Neural Network Based Syst...
A Comparison Study between Inferred State-Space and Neural Network Based Syst...Ahmed Momtaz Hosny, PhD
DEVELOPMENT OF A NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MIS...
DEVELOPMENT OF A NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MIS...Ahmed Momtaz Hosny, PhD
FUZZY LOGIC CONTROLLER TUNNING VIA ADAPTIVE GENETIC ALGORITHM APPLIED TO AIRCRAFT LONGITUDINAL MOTION.FUZZY LOGIC CONTROLLER TUNNING VIA ADAPTIVE GENETIC ALGORITHM APPLIED TO AIRC...
FUZZY LOGIC CONTROLLER TUNNING VIA ADAPTIVE GENETIC ALGORITHM APPLIED TO AIRC...Ahmed Momtaz Hosny, PhD
DEVELOPMENT OF FUZZY LOGIC LQR CONTROL INTEGRATION FOR AERIAL REFUELING AUTOPILOT.DEVELOPMENT OF FUZZY LOGIC LQR CONTROL INTEGRATION FOR AERIAL REFUELING AUTOP...
DEVELOPMENT OF FUZZY LOGIC LQR CONTROL INTEGRATION FOR AERIAL REFUELING AUTOP...Ahmed Momtaz Hosny, PhD
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project PresentationBoston Institute of Analytics
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This work investigates the performance on the aircraft control system during air refuel purposes of the Linear Quadratic Regulator (LQR) control alone, and the integration between fuzzy control and LQR. LQR is modern linear control that is suitable for multivariable state feedback and is known to yield good performance for linear systems or for nonlinear systems where the nonlinear aspects are presented. The fuzzy control is known to have the ability to deal with nonlinearities without having to use advanced mathematics. The LQR integrated fuzzy control (LQRIFC) simultaneously makes use of the good performance of the LQR in the region close to switching curve, and the effectiveness of the fuzzy control in region away from switching curve. A new analysis of the fuzzy system behavior presented helps to make possible precise integration of LQR features into the fuzzy control. The LQRIFC is verified by simulation to suppress the uncertain instability more effectively than the LQR alone besides minimizing the time of the mission proposed. The aerial refueling process has been divided into 3 basic stages (initial stability stage, tracking stage, alignment stage) to meet all the process requirements and constraints with minimizing the whole mission time period. GA has been used to tune the fuzzy logic controller parameters and some PID’s. The control strategy was applied to both aerial refueling methods. And the MATLAB simulation results show the validity and the efficiency of using the proposed control approach.
Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...
Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...Ahmed Momtaz Hosny, PhD
ABSTRACT
In recent years, there has been an increasing interest in the fusion of neural networks and fuzzy logic specially in missile control problems. A technique for the preliminary design of a control system is presented using a neurofuzzy approach for a highly nonlinear MIMO 5_DOF AIM 9R model. The model reflects cross coupling effects between the longitudinal and lateral motions. Two neural network controllers are used for the low level control of each motion separately. The control effort of these networks is then blended by a fuzzy logic controller to obtain the overall control action.The fuzzy controller which is a Mamdani type inference system has 25 rule base designed to cope with model uncertainties specially in cross coupling between lateral and longitudinal motions. A computer simulation is performed to compare between various control techniques. The result showed the effectiveness of the hybrid system compared to other control strategies where fuzzy systems or neural networks are used separately.
DEVELOPMENT OF NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MISS...
DEVELOPMENT OF NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MISS...Ahmed Momtaz Hosny, PhD
In this paper, system identifications of an unmanned aerial vehicle (UAV) based on inferred state space and multiple neural networks were presented. In this work an optimization approach was used to conclude an inferred state space and the multiple neural networks system identifications based on the genetic algorithms separately. The UAV is a multi-input multi-output (MIMO) nonlinear system. Models for such MIMO system are expected to be adaptive to dynamic behavior and robust to environment.A Comparison Study between Inferred State-Space and Neural Network Based Syst...
A Comparison Study between Inferred State-Space and Neural Network Based Syst...Ahmed Momtaz Hosny, PhD
The objective of this paper is to develop a customized autopilot system that enables a helicopter model to carry out an autonomous flight using on-board microcontroller. The main goal of this project is to provide a comprehensive controller design methodology, Modeling, simulation, guidance and verification for an unmanned helicopter model. The autopilot system was designed to demonstrate autonomous maneuvers such as flying over the planned waypoints with constant forward speed.Development of a Customized Autopilot for Unmanned Helicopter Model Using Gen...
Development of a Customized Autopilot for Unmanned Helicopter Model Using Gen...Ahmed Momtaz Hosny, PhD
Abstract:
In this paper, system identifications of an unmanned aerial vehicle (UAV) based on inferred state space and multiple neural networks were presented. In this work an optimization approach was used to conclude an inferred state space and the multiple neural networks system identifications based on the genetic algorithms separately.
The presented work is focusing on an inferred state space based system identification which is a new approach seldom used, but it is also easier and more stable compared with the multi-network based system identification during the modeling of dynamic behavior of nonlinear systems.
A Comparison Study between Inferred State-Space and Neural Network Based Syst...
A Comparison Study between Inferred State-Space and Neural Network Based Syst...Ahmed Momtaz Hosny, PhD
DEVELOPMENT OF A NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MIS...
DEVELOPMENT OF A NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MIS...Ahmed Momtaz Hosny, PhD
FUZZY LOGIC CONTROLLER TUNNING VIA ADAPTIVE GENETIC ALGORITHM APPLIED TO AIRCRAFT LONGITUDINAL MOTION.FUZZY LOGIC CONTROLLER TUNNING VIA ADAPTIVE GENETIC ALGORITHM APPLIED TO AIRC...
FUZZY LOGIC CONTROLLER TUNNING VIA ADAPTIVE GENETIC ALGORITHM APPLIED TO AIRC...Ahmed Momtaz Hosny, PhD
DEVELOPMENT OF FUZZY LOGIC LQR CONTROL INTEGRATION FOR AERIAL REFUELING AUTOPILOT.DEVELOPMENT OF FUZZY LOGIC LQR CONTROL INTEGRATION FOR AERIAL REFUELING AUTOP...
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Development of Fuzzy Logic LQR Control Integration for Full Mission Multistag...
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