Papers by tharwat O hanafy
Direct torque control of induction motor drives
ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics
Direct torque control (DTC) is an emerging technique for controlling PWM inverter-fed induction m... more Direct torque control (DTC) is an emerging technique for controlling PWM inverter-fed induction motor (IM) drives. It allows the precise and quick control of the IM flux and torque without calling for complex control algorithms. In principle, moreover, it requires only the knowledge of the stator resistance. The tutorial starts by reviewing the basic operation of an IM and of
In this paper, the Fusion of neural and fuzzy Systems will be investigated. Membership Function G... more In this paper, the Fusion of neural and fuzzy Systems will be investigated. Membership Function Generation and its mapping to Neural Network are introduced. An adaptive network fuzzy inference system (ANFIS) is introduced, and Multiple Inputs /Outputs Systems (Extended ANFIS Algorithm) is implemented. A Modification algorithm of ANFIS, Coupling of ANFIS called coactive neuro fuzzy system (CANFIS), is introduced and implemented using Matlab. The software of the modified algorithm of MIMO model identification is built. To test the validity of the modified algorithm ANFIS (CANFIS algorithm), an example is simulated from the numerical equation. The result of modified algorithm (CANFIS) showed a conformance with the simulated example and the root mean square (RMSE) is very small. (Tharwat O. S. Hanafy. A modified Algorithm to Model Highly Nonlinear System. Journal of American Science 2010;6(12):747-759). (ISSN: 1545-1003). http://www.americanscience.org.
Direct Torque Control of Induction Motor Drive
This paper presents direct torque control technique ,for induction motor .Direct torque control t... more This paper presents direct torque control technique ,for induction motor .Direct torque control technique in AC drive systems to obtain high performance torque control .In this paper Discrete space vector modulation technique (DSVM) is applied to 2 level inverter control in proposed direct toque controlled (DTC )induction motor drive having reduced torque ripple even at the low operating speeds and maintaining constant switching frequency has been studied and implemented .The proposed algorithm is very simple and easily implemented to control speed The proposed scheme is described clearly and simulation results are reported to demonstrate its effectiveness. The entire control scheme is implemented with Matlab/Simulink.
The growing importance of the knowledge-intensive service-based knowledge organizations and resul... more The growing importance of the knowledge-intensive service-based knowledge organizations and resultant dynamic capabilities, and facilitate service innovation Interval model can be used to describe nonlinear dynamic systems. Control of an inverted pendulum on a carriage which moves in a horizontal path, is one of the classic problems in the area of control. The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the inverted pendulum system. The main objective of this paper to keep the stabilization of the inverted pendulum based on the simplification of rule base. The proposed fuzzy control scheme successfully fulfills the control objectives and also has an excellent stabilizing ability to overcome the external impact acting on the pendulum system. This paper presents an application of how to design and validate a real time neuro fuzzy controller of complex a nonlinear dynamic system using the Matlab-Simulink Real-Time Workshop environment. Once the controller is obtained and validated by simulation, it's implemented to control the pendulum-cart system. The design and optimization process of neuro fuzzy controller are based on an extended learning technique derived from adaptive neuro fuzzy inference system (ANFIS). The design and implementation of this pendulum-cart control system has been realized under MATLAB/SIMULINK environment. The experimental results demonstrate the efficiency of the simplified design procedure and ensured stability of this system.
This paper presents an application of how to design and validate a real time neuro fuzzy controll... more This paper presents an application of how to design and validate a real time neuro fuzzy controller of complex a nonlinear dynamic system using the Matlab-Simulink Real-Time Workshop environment. Once the controller is obtained and validated by simulation, it's implemented to control the pendulum-cart system. Design of a neuro fuzzy controller is considered in this work because of its insensitivity to disturbances and uncertainties of model parameters. The design and optimization process of neuro fuzzy controller are based on an extended learning technique derived from adaptive neuro fuzzy inference system (ANFIS). The design and implementation of this pendulum-cart control system has been realized under MATLAB/SIMULINK environment. The experimental results demonstrate the efficiency of this design procedure and the ensured stability of the system. (Tharwat O. S. Hanafy. Design and validation of Real Time Neuro Fuzzy Controller for stabilization of
Energy Saving Strategies of an Efficient Electro- Hydraulic Circuit (A review)
In the past few years, considerable effort has been made to improve the power efficiency of elect... more In the past few years, considerable effort has been made to improve the power efficiency of electrohydraulic systems; many energy saving strategies have been successfully developed and used. However, most of them can only be useful in specific applications. For instance, displacement control and secondary control only focus on those systems in which the efficiency concerns are more important. Although these systems have very high efficiency, they are not designed for applications in which the flow rate is varied during the duty cycle. Compared with pump controlled systems and other energy efficient systems, the valve controlled system demonstrates good dynamic performance and controllability especially for inertia dominated loads but at the expense of power efficiency. For electrohydraulic circuits which employ load-sensing systems for example, the design objective has been made to combine the advantages of high dynamic performance with better energy utilization. However, this high ...
Identification of Uncertain Nonlinear MIMO Spacecraft Systems Using Coactive Neuro Fuzzy Inference System (CANFIS)
This paper attempts to present a neural inverse control design framework for a class of nonlinear... more This paper attempts to present a neural inverse control design framework for a class of nonlinear multiple-input multiple-output (MIMO) system with uncertainties. This research effort is motivated by the following considerations: (a) An appropriate reference model that accurately represents the desired system dynamics is usually assumed to exist and to be available, and yet in reality this is not the case often times; (b) In real world applications, there are many cases where controls are constrained within a physically allowable range, which presents another layer of difficulties to directly apply the reference model based inverse control; (c) It is difficult to consider optimal control even for the reference model as in general the analytic solution to the optimal control problem is not available. The simulation study has been focused on the identification of Multiple Input, Single Output (MISO) non-linear complex systems. This paper concentrates on the identification of Multiple ...
IOSR Journal of Engineering, May 1, 2014
This paper concentrates on the identification of Multiple Inputs Multiple Outputs (MIMO) system d... more This paper concentrates on the identification of Multiple Inputs Multiple Outputs (MIMO) system data with and without noise by means of a hybrid-learning rule, which combines the back propagation and the Least Mean Squared (LMS) to identify parameters. We construct a neuro fuzzy model structure, and generate the membership function from the measured data based on deterministic and soft computing. The four cases has been done. The first two cases for deterministic without and with noise have done. The second two cases for ANFIS without and with noise also have been done.. The MIMO system model is represented as a set of coupled input-output MISO models of the Takagi-Sugeno type. Neuro fuzzy model of the system structure is incorporated easily in the structure of the model. The simulation is used to implement a MIMO spacecraft system using Matlab for moment_yaw, moment_pitch, and moment_roll as input, and velocity in inertial axis as output. Experimental results are given to show the effectiveness of this Adaptive Neuro Fuzzy System (ANFIS) model. This paper attempts to present ANFIS control design framework for a class of nonlinear multiple-input multiple-output (MIMO) system with uncertainties. This research effort is motivated by the following considerations: (a) An appropriate reference model that accurately represents the desired system dynamics is usually assumed to exist and to be available, and yet in reality this is not the case often times; (b) In real world applications, there are many cases where controls are constrained within a physically allowable range, which presents another layer of difficulties to directly apply the reference model based inverse control; (c) It is difficult to consider optimal control even for the reference model as in general the analytic solution to the optimal control problem is not available. The simulation study has been focused on the identification of Multiple Input, Single Output (MISO) non-linear complex systems.
International Journal of Computer Applications
In this paper, the Fusion of neural and fuzzy Systems will be investigated. Membership Function G... more In this paper, the Fusion of neural and fuzzy Systems will be investigated. Membership Function Generation and its mapping to Neural Network are introduced. .System modeling based on conventional mathematical tools (differential equations) is not well suited for dealing with ill-defined and uncertain systems. By contrast, a fuzzy inference system employing fuzzy if-then rules can model the qualitative aspects of human knowledge and reasoning process without employing precise quantitative analyses. An adaptive network fuzzy inference system (ANFIS) is introduced, and Multiple Inputs /Outputs Systems (Extended ANFIS Algorithm) is implemented. A Modification algorithm of ANFIS, Coupling of ANFIS called coactive neuro fuzzy system (CANFIS), is introduced and implemented. The software of the modified algorithm of MIMO model identification is built and generated by me or added as a toolbox to matlab. To test the validity of the modified algorithm ANFIS (CANFIS algorithm), a coupled inputs-outputs example is simulated from the numerical equation. The result of modified algorithm (CANFIS) showed a conformance with the simulated example and the root mean square (RMSE) is very small.
Dynamic Modeling Process of Neuro Fuzzy System to Control the Inverted Pendulum System
Telkomnika Indonesian Journal of Electrical Engineering, Jun 21, 2014
The analysis and control of complex plants often requires the principles of qualitative process m... more The analysis and control of complex plants often requires the principles of qualitative process models since quantitative, namely analytical process models are not available. Qualitative modeling is one promising approach to the solution of difficult tasks automation if qualitative process models are not available. This contribution presents a new concept of qualitative dynamic process modeling using so called Dynamic Adaptive Neuro fuzzy Systems. This yields the framework of a new systems theory the essentials of which are given in further section of the paper. First, an identification method is presented, using a combination of linguistic knowledge.
Stabilization of inverted pendulum system using particle swarm optimization
2012 8th International Conference on Informatics and Systems, 2012
The proportional-integral-derivative (PID) controllers are the most popular controllers used in i... more The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal PID controller parameters for industrial process. PSO is a technique used to explore the search space of a given problem to find the settings or parameters required to maximize a particular objective. The focus will be on the application of the PSO into one of the popular problem setups in the engineering application area of control systems, which is called the inverted pendulum. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. The controller is obtained and validated by simulation; it's implemented to control the pendulum-cart system.
Usage of the new technology and new inventions are making the human life more and more convenient... more Usage of the new technology and new inventions are making the human life more and more convenient but alongside they are having several disadvantages as well. Air Pollution is a major drawback of a growth. Major sources of Air pollution are vehicle emitting lot of smoke, dust and other human generated garbage. Increasing So2 and No2 in air is the main cause of air pollution. In this paper, I am proposing a scheme to show the prediction of Air Pollution in urban areas using ANFIS controller applied to the historical data. I have also discussed the scheme for the prediction of O3 based on NO2 and SO2 measurements. Several researchers have proposed different techniques to predict the pollution including application of ANFIS. The techniques of artificial intelligence based in fuzzy logic and neural networks have been frequently applied together. The reasons to combine these two paradigms come out of the difficulties and inherent limitations of each isolated paradigm. Such an intelligent...
Simplifications of the Rule base for the stabilization of Inverted Pendulum System
TELKOMNIKA Indonesian Journal of Electrical Engineering, 2014
Control of an inverted pendulum on a carriage which moves in a horizontal path, is one of the cla... more Control of an inverted pendulum on a carriage which moves in a horizontal path, is one of the classic problems in the area of control. The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the inverted pendulum system. The main objective of this paper to keep the stabilization of the inverted pendulum based on the simplification of rule base. The proposed fuzzy control scheme successfully fulfills the control objectives and also has an excellent stabilizing ability to overcome the external impact acting on the pendulum system. The effectiveness of this controller is verified by experiments on a simple inverted pendulum with fixed cart length.
Qualitative modeling is one promising approach to the solution of difficult tasks automation if q... more Qualitative modeling is one promising approach to the solution of difficult tasks automation if qualitative process models are not available. This contribution presents a new concept of qualitative dynamic process modeling using so called Dynamic Adaptive Neuro fuzzy Systems. In contrast to common approaches of Adaptive Neuro Fuzzy modeling [1], the dynamic system is completely described in the neuro fuzzy domain: the neuro fuzzy information about the previous state is directly applied to compute the system's current state, i.e. the delayed neuro fuzzy output is feedback to the input without defuzzification. Knowledge processing in such dynamic neuro fuzzy systems requires a new inference method, the inference with interpolating rules. This yields the framework of a new systems theory the essentials of which are given in further section of the paper. First, an identification method is presented, using a combination of linguistic knowledge. Next, a stability definition for dynamic neuro fuzzy systems as well as methods for stability analysis is given. Finally, a neuro fuzzy model-based neuro fuzzy controller design method is developed. The identification of real problems and neuro fuzzy controller design for inverted pendulum system demonstrate the significance of the new systems theory.
This paper concentrates on the identification of Multiple Input Multiple Output (MIMO) system by ... more This paper concentrates on the identification of Multiple Input Multiple Output (MIMO) system by means of a hybrid-learning rule, which combines the back propagation and the Least Mean Squared (LMS) to identify parameters. We construct a neuro fuzzy model structure, and generate the membership function from the measured data. The MIMO system model is represented as a set of coupled input-output MISO models of the Takagi-Sugeno type. Neuro fuzzy model of the system structure is incorporated easily in the structure of the model. The simulation is used to implement a MIMO spacecraft system using Matlab for moment_yaw, moment_pitch, and moment_roll as input, and velocity in inertial axis as output. Experimental results are given to show the effectiveness of this Adaptive Neuro Fuzzy System (ANFIS) model. This paper attempts to present a neural inverse control design framework for a class of nonlinear multiple-input multiple-output (MIMO) system with uncertainties. This research effort is motivated by the following considerations: (a) An appropriate reference model that accurately represents the desired system dynamics is usually assumed to exist and to be available, and yet in reality this is not the case often times; (b) In real world applications, there are many cases where controls are constrained within a physically allowable range, which presents another layer of difficulties to directly apply the reference model based inverse control; (c) It is difficult to consider optimal control even for the reference model as in general the analytic solution to the optimal control problem is not available. The simulation study has been focused on the identification of Multiple Input, Single Output (MISO) non-linear complex systems.
The growing importance of the knowledge-intensive service-based knowledge organizations and resul... more The growing importance of the knowledge-intensive service-based knowledge organizations and resultant dynamic capabilities, and facilitate service innovation Interval model can be used to describe nonlinear dynamic systems. Control of an inverted pendulum on a carriage which moves in a horizontal path, is one of the classic problems in the area of control. The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the inverted pendulum system. The main objective of this paper to keep the stabilization of the inverted pendulum based on the simplification of rule base. The proposed fuzzy control scheme successfully fulfills the control objectives and also has an excellent stabilizing ability to overcome the external impact acting on the pendulum system. This paper presents an application of how to design and validate a real time neuro fuzzy controller of complex a nonlinear dynamic system using the Matlab-Simulink Real-Time Workshop environment. Once the controller is obtained and validated by simulation, it's implemented to control the pendulum-cart system. The design and optimization process of neuro fuzzy controller are based on an extended learning technique derived from adaptive neuro fuzzy inference system (ANFIS). The design and implementation of this pendulum-cart control system has been realized under MATLAB/SIMULINK environment. The experimental results demonstrate the efficiency of the simplified design procedure and ensured stability of this system.
In recent times, engineers have very well accepted soft computing techniques such as fuzzy sets t... more In recent times, engineers have very well accepted soft computing techniques such as fuzzy sets theory, neural nets, neuro fuzzy system, adaptive neuro fuzzy inference system (ANFIS), coactive neuro fuzzy inference system (CANFIS), evolutionary computing, probabilistic computing, Computational intelligence (CI), etc. for carrying out varying numerical simulation analysis. In last two decades, these techniques have been successfully applied in various engineering problems independently or in hybrid forms. The main objective of this paper is to introduce engineers and students about the latest trends in soft computing. Also they will help young researchers to develop themselves in futures.In recent years Computational intelligence (CI) has gained a widespread concern of many scholars emerging as a new field of study. CI actually uses the bionics ideas for reference, it origins from emulating intelligent phenomenon in nature. CI attempts to simulate and reappearance the characters of intelligence, such as learning and adaptation, so that it can be a new research domain for reconstructing the nature and engineering. The essence of CI is a universal approximator, and it has the great function of non-linear mapping and optimization.In this paper we give an overview of intelligent systems. We discuss the notion itself, together with diverse features and constituents of it. We concentrate especially on computational intelligence and soft computing.
Qualitative modeling is one promising approach to the solution of difficult tasks automation if q... more Qualitative modeling is one promising approach to the solution of difficult tasks automation if qualitative process models are not available. This contribution presents a new concept of qualitative dynamic process modeling using so called Dynamic Adaptive Neuro fuzzy Systems. In contrast to common approaches of Adaptive Neuro Fuzzy modeling [1], the dynamic system is completely described in the neuro fuzzy domain: the neuro fuzzy information about the previous state is directly applied to compute the system's current state, i.e. the delayed neuro fuzzy output is feedback to the input without defuzzification. Knowledge processing in such dynamic neuro fuzzy systems requires a new inference method, the inference with interpolating rules. This yields the framework of a new systems theory the essentials of which are given in further section of the paper. First, an identification method is presented, using a combination of linguistic knowledge. Next, a stability definition for dynamic neuro fuzzy systems as well as methods for stability analysis is given. Finally, a neuro fuzzy model-based neuro fuzzy controller design method is developed. The identification of real problems and neuro fuzzy controller design for inverted pendulum system demonstrate the significance of the new systems theory.
In this paper, the Fusion of neural and fuzzy Systems will be investigated. Membership Function G... more In this paper, the Fusion of neural and fuzzy Systems will be investigated. Membership Function Generation and its mapping to Neural Network are introduced. .System modeling based on conventional mathematical tools (differential equations) is not well suited for dealing with ill-defined and uncertain systems. By contrast, a fuzzy inference system employing fuzzy ifthen rules can model the qualitative aspects of human knowledge and reasoning process without employing precise quantitative analyses. An adaptive network fuzzy inference system (ANFIS) is introduced, and Multiple Inputs /Outputs Systems (Extended ANFIS Algorithm) is implemented. A Modification algorithm of ANFIS, Coupling of ANFIS called coactive neuro fuzzy system (CANFIS), is introduced and implemented. The software of the modified algorithm of MIMO model identification is built and generated by me or added as a toolbox to matlab. To test the validity of the modified algorithm ANFIS (CANFIS algorithm), a coupled inputs-outputs example is simulated from the numerical equation. The result of modified algorithm (CANFIS) showed a conformance with the simulated example and the root mean square (RMSE) is very small.
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Papers by tharwat O hanafy