استاذ مساعد في كلية الهندسه قسم الهندسة الكهربائية حاصل على شهادة الدكتوراه من المملكة المتحدة جامعة سسكس سنه 2012 في اختصاص هندسة السيطرة والحاسبات
وضع حساب المستخدم هذا هو تمت الموافقة

لم يقم هذا المستخدم بإضافة أي معلومات إلى ملفه الشخصي بعد.

استاذ مساعد
كلية الهندسة
البصرة
دكتوراه
38 سنة
سيطرة وحاسبات
البصرة
متزوج
الهندسة الكهربائية

تصميم وبناء منظومات التحكم الذكيه للسيطرة على الانظمة المختلفة

1. Title: Design of Fractional Order Controller Based on Evolutionary Algorithm for a Full Vehicle Nonlinear Active Suspension Systems.
International Journal of Control and Automation. Vol. 3 No. 4, December, 2010.
Abstract:
An optimal Fractional Order PID(FOPID) controller is designed for a full vehicle nonlinear active suspension system. The optimal values of FOPID controller parameters for minimizing the cost function are tuned using an Evolutionary Algorithm (EA), which offers an optimal solution to a multidimensional rough objective function. The fitness parameters of FOPID controller (proportional constant P, integral constant I, derivative constant D, integral order and derivative order µ) are selected from ranges of reliable values, depending on survival-to-the-fitness principle used in the biology science. A full vehicle nonlinear active suspension model including hydraulic actuators, nonlinear dampers and nonlinear springs has been proposed with structural and analytical details. The nonlinear frictional forces due to rubbing of piston seals with the cylinders wall inside the actuators are taken into account to find the real supply forces generated by the hydraulic actuator. The results of the full vehicle nonlinear suspension system using the FOPID controller are compared with the corresponding passive suspension system (system without controller). The controlled suspension system has been investigated under typical vehicle maneuvers: cruising on rough road surface, sharp braking and cornering. The results have clearly shown the effectiveness and robustness of the proposed controller.
2. Title: Adaptive Neuro-Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems.
2010 1st International Conference on Energy, Power and Control (EPC-IQ), College of Engineering, University of Basrah, Basrah, Iraq, November 30 - December 2, 2010.
Abstract:
The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIλDµ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.
3. Title: Design an Intelligent Controller for Full Vehicle Nonlinear Active Suspension Systems.
International Journal on Smart Sensing and Intelligent Systems Vol. 4, No. 2, June 2011.
Abstract:
The main objective of designed the controller for a vehicle suspension system is to reduce
the discomfort sensed by passengers which arises from road roughness and to increase the ride
handling associated with the pitching and rolling movements. This necessitates a very fast and
accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with
an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the
control objectives. The advantage of this controller is that it can handle the nonlinearities faster
than other conventional controllers. The approach of the proposed controller is to minimize the
vibrations on each corner of vehicle by supplying control forces to suspension system when
travelling on rough road. The other purpose for using the NF controller for vehicle model is to
reduce the body inclinations that are made during intensive maneuvers including braking and
cornering. A full vehicle nonlinear active suspension system is introduced and tested. The
robustness of the proposed controller is being assessed by comparing with an optimal Fractional
Order PIλDµ (FOPID) controller. The results show that the intelligent NF controller has improved
the dynamic response measured by decreasing the cost function.
4. Title: Design Neural Control System for Full Vehicle Nonlinear Active Suspension with Hydraulic Actuators.
International Journal of Advanced Research in Computer Science, Vol. 2, No. 2, Mar-Apr, 2011,266-274.
Abstract:
The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible, this paper deals with an artificial intelligence Neural Control technique to design a robust controller. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibration on each corner of vehicle by generating suitable control signals. This control signals will be used as input to the hydraulic actuators which will generate appropriate control forces to improve the vehicle performances. A full vehicle nonlinear active suspension system with hydraulic actuators is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIiDd (FOPID) controller. The results show that intelligent neural controller have improved dynamic response measured by a decreased cost function.
5. Title: Fuzzy Model Reference Learning Controller Based Full Vehicle Nonlinear Active Suspension Systems.
International Journal of Research and Reviews in Computer Science (IJRRCS) Vol. 2, No. 3, June 2011.
Abstract:
This paper is concerned with a full vehicle nonlinear active suspension system, in which each suspension unit consists of three components: a nonlinear spring, a nonlinear damper and a nonlinear hydraulic actuator. The control forces applied between the vehicle body and wheel axles are generated to achieve the objectives of both good riding performance and road handling stability. A Fuzzy Model Reference Learning Controller (FMRLC) is devised to adjust the hydraulic actuators forces to minimize the vertical displacement at each suspension point when travelling on rough roads and to reduce the inclination of the vehicle during sudden maneuvers such as sharp bending and braking. Four fuzzy sub-controllers are trained and applied to individual actuators in the vehicle suspension system. To demonstrate the effectiveness and robustness of the proposed controller, a comparison is made with the corresponding passive system without controllers in different cases of road profiles. Results show that the proposed controllers improve both the riding comfort and road handling stability.

6. Title: A neurofuzzy controller for full vehicle active suspension systems.
Journal of Vibration and Control Vol. 18, No. 12, 2011, 1837–1854.
Abstract:
The main objectives of designing the controller for vehicle suspension systems are to reduce the discomfort sensed by passengers that arises from road roughness and to increase the road handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as many control objectives as possible. This paper deals with an artificial intelligent neurofuzzy (NF) technique to design a robust controller. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibration on each corner of the vehicle by supplying control forces to the suspension system when travelling on a rough road. The other purpose of using the NF controller for the vehicle model is to reduce the body inclinations that are made during intensive maneuvers, including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparison with an optimal proportional-integral-derivative (PID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

7. Title: FPGA Based Adaptive Neuro Fuzzy Inference Controller For Full Vehicle
Nonlinear Active Suspension Systems.
International Journal of Artificial Intelligence & Applications (IJAIA), Vol.1, No.4, October 2010.
Abstract:
A Field Programmable Gate Array (FPGA) is proposed to build an Adaptive Neuro Fuzzy Inference System (ANFIS) for controlling a full vehicle nonlinear active suspension system. A Very High speed integrated circuit Hardware Description Language (VHDL) has been used to implement the proposed controller. An optimal Fraction Order PIλDµ (FOPID) controller is designed for a full vehicle nonlinear active suspension system. Evolutionary Algorithm (EA) has been applied to modify the five parameters of the FOPID controller (i.e. proportional constant Kp, integral constant Ki, derivative constant Kd, integral order λ and derivative order µ). The data obtained from the FOPID controller are used as a reference to design the ANFIS model as a controller for the controlled system. A hybrid approach is introduced to train the ANFIS. A Matlab Program has been used to design and simulate the proposed controller. The ANFIS control parameters obtained from the Matlab program are used to write the VHDL codes. Hardware implementation of the FPGA is dependent on the configuration file obtained from the VHDL program. The experimental results have proved the efficiency and robustness of the hardware implementation for the proposed controller. It provides a novel technique to be used to design NF controller for full vehicle nonlinear active suspension systems with hydraulic actuators.
8. Title: The Energy Regeneration of Electromagnetic Energy Saving Active Suspension In Full Vehicle With Neurofuzzy Controller.
International Journal of Artificial Intelligence & Applications (IJAIA), Vol.2, No.2, April 2011.
Abstract:
To improve the vehicle performance such as ride comfort and road handling, the active suspension system should be used. However, the current active suspension system has a high energy consumption therefore reducing the fuel economy. In this paper the vibration excited by road unevenness is treated as a source of mechanical energy. It is being converted into electrical energy to compensate for the energy consumption by the active suspension. To achieve this task, an electromagnetic active suspension system has been introduced. The power generated from this device has been used as input power of the pump of the hydraulic actuators. Adaptive neurofuzzy controllers have been designed to generate a signal to control the valves of the hydraulic actuators.
9. Title: Design an Optimal PID Controller using Artificial Bee Colony and Genetic Algorithm for Autonomous Mobile Robot.
International Journal of Computer Applications (0975 – 8887) Volume 100 – No.16, August 2014.
Abstract:
Target tracking is a serious function for an autonomous mobile robot navigating in unknown environments such as disaster areas, projects sites, and any dangerous place which the human cannot reach. This paper deals with modified the parameters of PID controller using Artificial Bee Colony (ABC) and Genetic Algorithm (GA) for path tracking of autonomous mobile robot. Two PID control are designed, one for speed control and the other for azimuth control. The MATLAB program is used to simulate the autonomous mobile robot model with optimal PID controllers, ABC algorithm and GA. To test the effectiveness of the proposed controllers, two path trajectories have been chosen: circular path and sine wave path. The results have clearly shown the effectiveness and good performances of the PID controllers which are tuned using ABC algorithm than using GA.
10. Title: Design Neurofuzzy With PID Controllers for An Autonomous Mini-Helicopter System.
Basrah Journal for Engineering Sciences. Vol. 14, No.1, 2014.
Abstract:
In this paper a combining Neurofuzzy and PID controllers have been employed for controlling the positions and rotational motions of the mini-helicopter system. Due to the strong coupling between the state variables of the mini-helicopter model, therefore, it is not suitable to design single controller for regulating the positions and rotational motions of the given model. To solve this problem, three neurofuzzy controllers are designed for the lateral, longitudinal and heave motion; and three classical PID controllers are proposed for attitude control. Nine rules are suggested for each neurofuzzy network depends on the previous knowledge/experiences of expert human pilot. The simulation results show that the proposed controllers are very effective to control the hovering, position and forward flight of the mini-helicopter system.
11. Title: A Self Learning Fuzzy Logic Controller for Ship Steering System.
Iraq J. Electrical and Electronic Engineering. Vol.8 No.1, 2012.
Abstract:
A self learning fuzzy logic controller for ship steering systems is proposed in this paper. Due to the high nonlinearity of ship steering system, the performances of traditional control algorithms are not satisfactory in fact. An intelligent control system is designed for controlling the direction heading of ships to improve the high efficiency of transportation, the convenience of maneuvering ships, and the safety of navigation. The design of fuzzy controllers is usually performed in an ad hoc manner where it is hard to justify the choice of some fuzzy control parameters such as the parameters of membership function. In this paper, self tuning algorithm is used to adjust the parameters of fuzzy controller. Simulation results show that the efficiency of proposed algorithm to design a fuzzy controller for ship steering system.
12. Title: FPGA Based Modified Fuzzy PID Controller for Pitch Angle of
Bench-top Helicopter.
Iraq J. Electrical and Electronic Engineering. Vol.8 No.1 , 2012.
Abstract:
Fuzzy PID controller design is still a complex task due to the involvement of a large
number of parameters in defining the fuzzy rule base. To reduce the huge number of fuzzy rules
required in the normal design for fuzzy PID controller, the fuzzy PID controller is represented as
Proportional-Derivative Fuzzy (PDF) controller and Proportional-Integral Fuzzy (PIF) controller
connected in parallel through a summer. The PIF controller design has been simplified by replacing
the PIF controller by PDF controller with accumulating output. In this paper, the modified Fuzzy PID controller design for bench-top helicopter has been presented. The proposed Fuzzy PID controller has been described using Very High Speed Integrated Circuit Hardware Description Language (VHDL) and implemented using the Field Programmable Gate Array (FPGA) board. The bench-top helicopter has been used to test the proposed controller. The results have been compared with the conventional PID controller and Internal Model Control Tuned PID (IMC-PID) Controller.
Simulation results show that the modified Fuzzy PID controller produces superior control
performance than the other two controllers in handling the nonlinearity of the helicopter system. The
output signal from the FPGA board is compared with the output of the modified Fuzzy PID controller to show that the FPGA board works like the Fuzzy PID controller. The result shows that the plant responses with the FPGA board are much similar to the plant responses when using simulation software based controller.
13. Title: Design of Neurofuzzy Self Tuning PID Controller for Antilock Braking Systems.
Journal of Babylon University/Engineering Sciences/ No.(4)/ Vol.(22): 2014.
Abstract:
In this paper, a Neurofuzzy self tuning PID controller for wheel slip ratio control has been
designed based on a quarter vehicle model. The proposed control structure consists of a Neurofuzzy
controller and conventional PID controller, which has self tuning capabilities. The parameters of the
PID controller (Kp, Kd and Ki) can be self-tuned on-line with the output of the system under control.
Variations in the values of weight, the friction coefficient of the road, road inclination and other
nonlinear dynamic parameters may highly affect the performance of the Antilock Braking Systems (ABS). The conventional PID controller with fixed parameters cannot overcome these effects; therefore, the PID controller with adaptable parameters has been used. The paper develops a self tuning PID control scheme with application to ABS via combinations of fuzzy logic systems and neural networks. The performance of the Neurofuzzy self tuning PID controller based ABS is demonstrated by simulation for different road conditions: Snowy road, Wet asphalt, Dry asphalt; and transitions between such conditions, e.g. when emergency braking occurs and the road switches from snowy to wet. Robustness against road conditions is examined via numerically test results of the ABS controlled by proposed scheme are compared with the results of the ABS controlled by optimal PID controller. Simulation results show good performance of the proposed controller.

14. Title: Hardware Implementation of the Neural Network Predictive Controller for Coupled Tank System.
American Journal of Electrical and Electronic Engineering, 2014, Vol. 2, No. 1, 40-47.
Abstract:
In this paper, a neural network based predictive controller is designed for controlling the liquid level of the coupled tank system. The controlled process is a nonlinear system; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant to predict future plant performance. The simulation results are compared with PID control. The results show that the effectiveness of using the neural predictive controller for the coupled tank system. The Simulink Toolbox in MATLAB has been used to simulate the controlled system with the proposed controller. The VHDL has been used to describe the implementation of neural controller. Xilinx ISE Project Navigator Version 10.1 is used to obtain the compilation and timing test results as well as the synthesized design. The hardware implementation of the neural network predictive controller using FPGA board is proposed. To make sure that the FPGA board works like the simulated neural predictive controller, MATLAB program is used to compare between the set of the data that are obtained from the ModelSim program and the set of the data that are obtained from the MATLAB Simulink model. Simulation results show that the FPGA board can be used as neural
predictive controller for controlling the liquid level of the coupled tank system.
15. Title: Pitch Angle Control Design of Wind Turbine Using Fuzzy-Art Network.
Journal of Engineering and Development, Vol. 18, No.4, July 2014, ISSN 1813- 7822.
Abstract:
Wind energy is by far the fastest-growing renewable energy resource. The power extracted from the wind can be optimized or restricted by adjusting the blades pitch angles of the wind turbine. The wind turbine model is highly nonlinear; therefore, an intelligent controller should be designed to adjust the pitch angles of the blades. In this paper, the fuzzy- ART (ART for Adaptive Resonance Theory) network has been used to control the angle between the incoming wind direction and the chord line of the blade. Simulation results show that the proposed controller is very effective to adjust the pitch angles. Keywords: Wind turbine, Pitch angle control, Fuzzy-ART networks.

16. Title: Implementation and Design of Fuzzy Supervisory Controller for Mobile Robot Manipulator.
Abstract:
The Mobile Manipulator Robot (MMR) has many applications in different aspects of the life, for example, grasping and transporting, mining, military, manufacturing, construction and others. The benefits of MMR rise in dangerous place where the human cannot reach such as disaster areas and dangerous projects sites. In this work, the PID controller is combined with Fuzzy Logic Controller (FLC) to structure the Fuzzy Supervisory Controller (FSC) to overcome the drawbacks of PID controller and to obtain the advantages of FLC. Two approaches are suggested for the navigation of Autonomous Mobile Robot (AMR). These are; goal reaching fuzzy control (GRFC) and the obstacle avoidance fuzzy control (OAFC). The hardware implementation of the AMR is performed using AVR ATmega32 microcontroller, two DC motors, light dependent resistor (LDR) and five Infra Red sensors. While, the Laboratory robot arm with some fabrications is used as manipulator arm with a five degrees-of-freedom. Then a microcontroller is employed to implement the proposed controller for MMR. The designed MMR is tested in real environments and give a good navigation.

هندسة التحكم المتقدم المرحلة الرابعة و تحليل العددي لطلبة الماجستير

university of basrah website الموقع الرسمي لجامعة البصرة