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Control and Intelligent Systems, Vol. 44, No. 1, 2016 OPTIMIZED AUTOMATED TRACKING OF A MOVING OBJECT WITH A ROBOTIC EYE SYSTEM Ahmed Alostaz∗ and Basil Hamed∗ Abstract This automated object tracking system is very essential in terms of surveillance system and many more applications. In this paper, the design of fuzzy proportional-integral- derivative controller is proposed to control a robot eye to achieve a real-time video object tracking. The controller uses vision-based object tracking as feedback and generates velocity commands for the robot eye to keep the object of interest (OBI) in video at the centre of the image frame. The particle filter (PF) is used to track OBI in video by estimating its position in image frame. A new optimized bootstrap PF (OBPF) is introduced solving problem of PF on video object tracking for rigid motion …

Recently, the particle filter (PF) is used to track video object by estimating its position in image frame, but it suffers some problems: degeneracy phenomenon and sample impoverishment. In this paper, a new optimized bootstrap particle filter (OBPF) is introduced to solve the problem of particle filter on video object tracking for rigid motion. Where adaptive Chemostic step, bacteria use in adaptive bacteria foraging based on particle swarm optimization algorithm (ABF-PSO), is tackled to improve predict step in PF. Moreover, particle swarm optimizations (PSO) is used in resample step of OBPF. The comparison among OBPF, particle filter based on bacteria foraging optimization (PF-BFO) and particle filter based particle swarm optimization (PF-PSO) on video object tracking is presented in this paper by using Matlab program. The results show that OPF method presents outstanding performance versus PF-PSO and PF-BFO. It has the best velocity and the best accuracy, so it is suitable for real time tracking.

Gaza city has 21 water wells within a radius of 40 square kilometer that feeds the city with water. The control of water wells still stays in a manual state and some of these wells are on isolated area. A water distribution system must be operated properly so that it performs at an acceptable level of service. This paper describes the application of supervisory control and data acquisition (SCADA) technology on water well control monitoring system in Gaza-strip. The proposed system is designed to solve the major problems in water distribution in Gaza city, through solving some weak points such as accuracy and delay in gathering data by using SCADA, and synchronization between water distribution and electricity existence. Solving the existing water distribution problem by using INTEGRAXOR SCADA program which is pure webserver that it doesn’t need any additional programs. Also using programmable logic controller (PLC) to act as interface between hardware component and software program. In addition controlling via mobile had been added to the system as a second method

In this paper Fuzzy controller is used to control Heating, Ventilating and Air Conditioning (HVAC) System, which is time varying nonlinear system. This controller consists of two fuzzy levels. The first fuzzy level controls two varying feedback parameters (Air temperature and Air quality) and to make the controller adequate with these changes. While the second level controls the Error and Change of Error that comes from first fuzzy control level. In this research, a hierarchical Sugeno fuzzy controller type will be designed. The proposed structure is used to reduce the number of rules and the computational time required for the simulation processes, so it is suitable for nonlinear temperature control for low energy large buildings with features such as large capacity and longtime delay. The controller is developed using a computer simulation of a virtual building contains most parameters of a real building. Fuzzy rules are learned from experts and system performance observations. The proposed controller is tested using Matlab/Simulink environment, the results show that the Sugeno controller has a good response. Achieving these purposes will increase the thermal comfort and reduce energy consumption

This paper presents an optimum approach for designing of fuzzy controller for nonlinear system using FPGA technology with Genetic Algorithms (GA) optimization tool. A magnetic levitation system is considered as a case study and the fuzzy controller is designed to keep a magnetic object suspended in the air counteracting the weight of the object. Fuzzy controller will be implemented using FPGA chip. Genetic Algorithm (GA) is used in this paper as optimization method that optimizes the membership, output gain and inputs gains of the fuzzy controllers. The design will use a highlevel programming language HDL for implementing the fuzzy logic controller using the Xfuzzy tools to implement the fuzzy logic controller into HDL code. This paper, advocates a novel approach to implement the fuzzy logic controller for magnetic ball levitation system by using FPGA with GA.

This paper presents an optimum approach for designing of fuzzy controller for nonlinear system using Genetic Algorithms (GA). In this paper, a magnetic levitation system is considered as a case study and the controller is designed to keep a magnetic object suspended in the air counteracting the weight of the object. Genetic Algorithm (GA) is used in this paper as optimization method that optimizes the membership, output gain and inputs gains of the fuzzy controllers. The proposed algorithms are implemented using Matlab and Simulink

This paper deals with the development and implementation of a framework of SCADA using LabVIEW based Data Acquisition and Management. A SCADA system is described in terms of architecture, process interfaces, functionality, and application development facilities. The system is low on system requirements and easy to implement. This paper describes configuration of Remote Terminal Units to access and transmit real time data over the Intranet, logging all the data in a historian for future references at a centralized location, keeping the human operator updated via Human Machine Interface, and providing measures for any emergency and enabling control via SMS giving true remote control capabilities. In addition, the project can automatically run the suitable generator from his reading of the foundation power; in short it’s the best solution of the foundation electricity problem.

This paper presents sun tracking generating power system designed and implemented in real time. A tracking mechanism composed of photovoltaic module, stepper motor, sensors, input/output interface and expert FLC implemented on PIC, that to track the sun and keep the solar cells always face the sun in most of the day time. The proposed sun tracking controller is tested using Matlab/Simulink program, the results show that the controller have a good response

The cell has optimum operating point to be able to get maximum power. To obtain Maximum Power from photovoltaic array, photovoltaic power system usually requires Maximum Power Point Tracking (MPPT) controller. This paper provides a small power photovoltaic control system based on fuzzy control with FPGA technology design and implementation for MPPT. The system composed of photovoltaic module, buck converter and the fuzzy logic controller implemented on FPGA for controlling on/off time of MOSFET switch of a buck converter. The proposed maximum power point tracking controller for photovoltaic system is tested using model designed by Matlab/Simulink program with graphical user interface (GUI) for entering the parameters of any array model using information from its datasheet, Simulation and experimental results show that performance of the fuzzy controller with FPGA in a maximum power tracking of a photovoltaic array can be made use of in several photovoltaic products and obtain satisfied result.

People with physical disabilities face a lot of problems in communication with their fellow human beings. In this paper, the design of an eye-controlled system with an emphasis on the Human-Computer-Interface (HCI) based on Electroencephalography (EEG) has been presented. The basis for this design is the electric potentials generated by the human eye. After this potential is sensed by electrodes, the signals are amplified, filtered, shifted, and sent into the PIC microcontroller. Also, additional processes were designed to overcome the sources of EEG signals variation. The signals captured using sensors, are first amplified, and then noise is removed and then digitized, before being transferred to PC for software interfacing Using PIC16F877A and RS232 serial communication. Visual basic software is used to build a nonconventional keyboard layout.

PID control is widely used in industrial applications although it is a simple control method. Stability of PID controller can be guaranteed theoretically, and zero steady-state tracking error can be achieved for linear plant in steady-state phase. Computer simulations of PID control algorithm have revealed that the tracking error is quite often oscillatory, however, with large amplitudes during the transient phase. To improve the performance of the PID controllers, several strategies have been proposed, such as adaptive and supervising techniques. Fuzzy control methodology is considered as an effective method to deal with disturbances and uncertainties in terms of ambiguity. Fuzzy PID controller combining fuzzy technology with traditional PID control algorithm to become more effective in artificial intelligence control [1],[2]. The most common problem resulted early depending on the complexity of Fuzzy Logic Control (FLC) is the tuning problem. It is hard to design and tune FLCs manually for the most machine problems especially used in industries like nonlinear systems. For alleviation of difficulties in constructing the fuzzy rule base, there is the conventional nonlinear design method [3] which was inherited in the fuzzy control area, such as fuzzy sliding control, fuzzy scheduling [4],[5], and adaptive fuzzy control [6],[7]. The error signal for most control systems is available to the controller if the reference input is continuous. The analytical calculations present two-inputs FLC employing proportional error signal and velocity error signal. PID controller is the most common controller used in industries, most of development of fuzzy controllers revolve around …

The new method of proportional‐integral‐derivative (PID) controller is proposed in this paper for a hybrid fuzzy PID controller for nonlinear system. The important feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy fed PID controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller contains one part. This single part of the rules uses the Takagi–Sugeno method for solving the nonlinear problem. The simulation results of a nonlinear system show that the performance of a fed PID Hybrid Takagi‐Sugeno fuzzy controller is better than that of the conventional fuzzy PID controller or Hybrid Mamdani fuzzy FED PID controller.

The design of controllers for nonlinear systems in industry is a complex and difficult task. The development of nonlinear control techniques has been approached in many different ways with varied results. One approach which has been shown promise for solving nonlinear control problems is the use of fuzzy logic control. Fuzzy controllers have proven suitable for many nonlinearly modeled industrial processes (as have been successfully demonstrated in Japan and Europe) such as linguistically controlled devices and systems that cannot be precisely described by mathematical formulae. A unified approach for comparing the performance of fuzzy and nonfuzzy (classical) controller designs for nonlinear systems is presented in this dissertation; also, two fuzzy controllers are presented and compared with classical controllers and then compared with each other. These two fuzzy controllers are the Mamdani (for the two …

Inverters are used widely nowadays because of many of renewable energy source available. These energy source can provide DC voltage like solar panels and the real need of storage energy devices as batteries which also provide DC voltage. Traditional DC-AC inverters have main limitation, this limitation is that AC output voltage is limited below and cannot exceed the DC-rail voltage. To overcome this limitation and usage of transformer for boosting; in this thesis I explained briefly the Z-source network technique in inverters which can work buck/boost and inverting in one stage without needing to boost DC firstly and then invert or use the same DC to invert and then use transformer to boost. Later Quasi-Z-Source network was developed, to operate better than Z-Source inverter by lower component ratings, reduced voltage stress, reduced component count and simplified control strategies. It is common that the M (Modulation index) plays the unique effector in controlling Quasi-Z-Source inverters and Do (Shoot through duty cycle) is related to M ; this thesis separates M & Do as each of them is output control signal of controller. This step added more limitation to component rating because of reducing the current in inductors to half. Fuzzy Logic control used as a controller for the inverter .This thesis used mamdani method with 3 VDC ,IDC and Vc1 input and 2 outputs M and Do ,and centroid method of defuzzification was used .The designed inverter in this thesis could keep the output voltage 208 AC rms 60 Hz with wide range of variable DC input (200-400) v DC.

Fuzzy logic control (FLC) systems have been tested in many technical and industrial applications as a useful modeling tool that can handle the uncertainties and nonlinearities of modern control systems. The main drawback of the FLC methodologies in the industrial environment is challenging for selecting the number of optimum tuning parameters. In this dissertation, a method has been proposed for finding the optimum membership functions, output gain and inputs gain of a fuzzy system using genetic algorithm (GA). A synthetic algorithm combined from fuzzy logic control and GA is used to design a controller for DC-DC converter. To exhibit the effectiveness of proposed algorithm, it is used to optimize the triangle membership functions of the fuzzy model DC-DC converter system as a case study. It is clearly proved that the optimized fuzzy controllers provided better performance than a fuzzy model for the same system in terms of settling time, steady state error and ripple voltage at the output response. The evaluation of the output has been carried out by software simulation using MATLAB Simulink.

Sliding Mode Fuzzy Controller (SMFC) which has sliding surface gains is on-line tuned by minimum fuzzy inference algorithm. The main goal is to guarantee acceptable trajectories tracking between the robot manipulator actual and desired trajectory. An educational simulation tool objective is to make practical teaching, learning kinematics and dynamic modeling and to apply different controllers on robot manipulator. Pure Sliding Mode Controller (SMC) and Sliding Mode Fuzzy Controller have difficulty in handling unstructured model uncertainties. It is possible to solve this problem by combining sliding mode fuzzy controller and fuzzy-based tuning. Since the sliding surface gain is adjusted by fuzzy based tuning method, the sliding surface slope updating factor of fuzzy-based tuning part can be changed with the changes in error and change of error rate between half to one. Sliding surface gain is adapted on-line by sliding surface slope updating factor. In pure sliding mode controller and sliding mode fuzzy controller, the sliding surface gain is chosen by trial and error, which means that pure sliding mode controller and sliding mode fuzzy controller must have a prior knowledge of the system uncertainty. Fuzzy-based tuning sliding mode fuzzy controller is a model-free stable control for robot manipulator. It is a one of the best solution to eliminate chattering phenomenon with switching function in structure and unstructured uncertainties.

Cardiovascular diseases are major causes of morbidity and mortality in the developed countries. One of the cardiac diseases, bradycardia sometimes results in fainting, shortness of breath, and if severe enough, death. It is defined as a heart rate less than 60 bpm. Implantable cardiac devices such as pacemakers are widely used nowadays. They have become a therapeutic tool used worldwide with more than 250, 000 pacemaker implants every year. In this thesis:Design a model for heart, sensing system and Mamdani fuzzy controller to generate electric pulses that mimic the natural pacing system of the heart, maintain an adequate heart rate by delivering controlled, rhythmic electrical stimuli to the chambers of the heart, and prevent human from being harmed by low heart rate. Design and implementation of a model of the heart that can be controlled to be the heart of a patient suffering from a decrease in heart rate (Bradycardia), design and implementation of sensing system to calculate the heart rate per minute and it is considered as an input to the controller, and design and implementation of Mamdani fuzzy controller to generate electric pulses that mimic the natural pacing system of the heart, maintain an adequate heart rate by delivering controlled, rhythmic electrical stimuli to the chambers of the patient heart. The proposed controller is tested using Matlab/Simulink program, the results show that the Mamdani fuzzy logic controller has a good response when compared with fuzzy proportional-integral-derivative controller.

Renewable energy sources play an important role in electric power generation. There are various renewable sources, which are used for electric power generation, such as solar energy, wind energy, geothermal etc. Solar energy is a good choice for electric power generation, which is viewed as clean and renewable source of energy for the future since the solar energy is directly converted into electrical energy by solar photovoltaic modules which is suitable for obtaining higher power output. There are two major approaches: sun tracking and maximum power point tracking to maximizing the power output of the solar arrays. These two methods need efficient intelligent controller such as Fuzzy Logic Controller (FLC). The advantage of the fuzzy logic control is that it does not strictly need any mathematical model of the plant. It is based on operator experience, and it is very easy to apply. Hence, many complex systems can be controlled without knowing the exact mathematical model of the plant. In addition, fuzzy logic simplifies dealing with nonlinearities in systems. The advantage of fuzzy logic control is that the linguistic system definition becomes the control algorithm. In this thesis: Two Sugeno fuzzy logic controllers are designed to increase the energy generation efficiency of solar cells. These controllers are, sun-tracking controller and maximum power point tracking controller with Genetic Algorithm (GA) to optimize the membership, output gain and inputs gain of the fuzzy controllers. Solar tracking system uses a stepper motor as the drive source to rotate the solar panel. The position of the sun is determined by using a tracking sensor, the sensor reading is converted from analog to digital signal, and then it passed to a fuzzy logic controller. The controller output is connected to the driver of the stepper motor to rotate PV panel in one axis until it faces the sun. The proposed sun-tracking controller is tested using Matlab/Simulink program, the results show that the Sugeno controller has a good response when compared with Mamdani controller. Maximum power point tracking system uses dc-to-dc converter to compensate the output voltage of the solar panel to keep the voltage at the value, which maximizes the output power. MPP fuzzy logic controller measures the values of the voltage, and current at the output of the solar panel. Then, calculates the power from the relation (P=V*I) to extract the inputs of the controller, and the crisp output of the controller represents the duty cycle of the pulse width modulation for controlling on/off time of MOSFET switch of a buck converter. The proposed maximum power point tracking controller for grid-connected photovoltaic system is tested using model designed by Matlab/Simulink program and the results show that the Sugeno controller with GA has a response better than Mamdani controller applied to the same system.

The advantage of fuzzy logic control is that it does not strictly need any mathematical model of the plant. It is based on operator experience, and it is very easy to apply. Hence, many complex systems can be controlled without knowing the exact mathematical model of the plant. In addition, fuzzy logic simplifies dealing with nonlinearities in systems. Fuzzy controller is used to control Heating, Ventilating and Air Conditioning (HVAC) System, which is time varying nonlinear system. This controller consists of two fuzzy levels. The first fuzzy level is controls two varying feedback parameters (Air temperature and Air quality) and to make the controller adapt with these changes. While the second level is controls the Error and Change of Error that comes from first level. In this thesis, a hierarchical Sugeno fuzzy controller will be designed .The proposed structure is used to reduce the number of rules and the computing time required for the simulation processes, so it is suitable for nonlinear temperature control for large low energy buildings with features such as large capacity and longtime delay. The controller is developed using a computer simulation of a virtual building contains most parameters of a real building. Fuzzy rules are learned from experts and system performance observations. The proposed controller is tested using Matlab/Simulink environment , the results show that the Sugeno controller has a good response compared to the Mamdani controller, which applied to the same system. Achieving these purposes will increase the thermal comfort and reduce energy consumption.

Proper control for low energy buildings is more difficult than conventional buildings due to their complexity and sensitivity to operating conditions. Therefore, Suitable controller is essential for better performance. In this thesis, Adaptive Hierarchical Fuzzy control is used to control Heating, Ventilating and Air Conditioning (HVAC) System which is time varying nonlinear system. The proposed Controller is capable of maintaining comfort conditions under time varying thermal loads. Adaptive Hierarchical Fuzzy is consist of two levels; first fuzzy level is to control two varying feedback parameters (Air temperature and Air quality) and to make the controller adapt with these changes and make it easy to consider any energy from renewable energy sources; the second fuzzy level is to control the Error and Change of Error that comes from first level. A hierarchical structure is used to reduce the number of rules, easily to extract fuzzy rules, to trim redundant information and reduce the computing time required for the simulation processes, so it is suitable for nonlinear temperature control with large low energy buildings with features such as large capacity and longtime delay. The controller is developed using a computer simulation of a virtual building contains most parameters of a real building. Fuzzy rules are learned from experts and system performance observations. Matlab program is used to simulate HVAC system and to see the results of the new controller.

The design of controllers for non- linear systems in industry is a complex and difficult task. The development of non-linear control techniques has been approached in many different ways with varied results. One approach, which has shown promise for solving nonlinear control problems, is the use of fuzzy logic control. This Thesis will discuss the Magnetic Levitation (Maglev) models as an example of non linear systems. It will also show the design of fuzzy logic controllers for this model to prove that the fuzzy controller can work properly with nonlinear system. Genetic Algorithm (GA) is used in this thesis as optimization method that optimize the membership, output gain and inputs gain of the fuzzy controllers. The result of fuzzy controller with GA optimization will be compared with H2 controllerwhich is one of optimal control techniques, and will try to prove that fuzzy controller with GA optimization can gives better performance over H2 controller. Finally fuzzy controller will be implemented using FPGA chip. The design will use a high-level programming language HDL for implementing the fuzzy logic controller using the Xfuzzy CAD tools to implement the fuzzy logic controller into HDL code.

Solar energy is viewed as clean and renewable source of energy for the future. So the use of Photovoltaic systems has increased in many applications. That need to improve the materials and methods used to harness this power source. There are two major approaches; sun tracking and maximum power point tracking. These two methods need efficient controllers. The controller may be conventional or intelligent such as Fuzzy Logic Controller (FLC). FLCs have the advantage to be relatively simple to design as they do not require the knowledge of the exact model and work well for nonlinear system. To implement this controller, Field Programmable Gate Array (FPGA) can be used. This method has many advantages over classical microprocessors. In this research, Two fuzzy logic controllers are fabricated on modern FPGA card (Spartan-3AN, Xilinx Company, 2009) to increase the energy generation efficiency of solar cells. These controllers are, sun tracking controller and maximum power point tracking controller. Sun tracking generating power system is designed and implemented in real time. A tracking mechanism composed of photovoltaic module, stepper motor, sensors, input/output interface and expert FLC controller implemented on FPGA, that to track the sun and keep the solar cells always face the sun in most of the day time. The proposed sun tracking controller is tested using Matlab/Simulink program, the results show that the controller have a good response. Maximum power point tracking system is designed and implemented. The system composed of photovoltaic module, buck converter and the fuzzy logic controller implemented on FPGA for controlling on/off time of MOSFET switch of a buck converter. The proposed maximum power point tracking controller for grid-connected photovoltaic system is tested using model designed by Matlab/Simulink program with graphical user interface (GUI) for entering the parameters of any array model using information from its datasheet, the results show that the controller have a response better than conventional controller applied on the same system.

The internal model control (IMC) philosophy relies on the internal model principle, which states that control can be achieved only if the control system encapsulates, either implicitly or explicitly, some representation of the process to be controlled. In particular, if the control scheme is developed based on an exact model of the process, then perfect control is theoretically possible. A new approach of control design of internal model controller is proposed in this thesis. The proposed design method focuses on modifying the old general structure of IMC and develops a new model structure while saving the same general concept of using the invertible version of the system in the controller design. The new approach combines the IMC structure and the traditional structure of a control problem and this demonstrates an excellent performance and behavior against different disturbance inputs and model uncertainty presented in model parameter mismatch. Beside that a smith predictor is added to promote the design to compensate the delayed time systems. Also a proposed stabilizer has mentioned to deal with unstable systems. The research browses the pendulum system and gets its transfer function to be the base of the design, which examines our proposed controller. Matlab/simulink is used to simulate the procedures and validate their performance. The results approved the robustness of the new method and got graded responses when compared with others. Furthermore, a comparison between the IMC and new modified IMC was conducted and shows that the new IMC is superior to old structure in terms of time delay compensation and response specifications.

The design of controllers for nonlinear systems in industry is a complex and difficult task. The development of nonlinear control techniques has been approached in many different ways with varied results. One approach which has shown promise for solving nonlinear control problems is the use of fuzzy logic control. This thesis proposes a new method utilizing proportional–integral-derivative (PID) control as a hybrid fuzzy PID controller for nonlinear system. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy Fed PID controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller contains one part for a non optimized controller. This single part of the rules uses the Takagi-Sugeno method for solving the nonlinear problem and compares it to the mamdani method. The number of fuzzy rules are minimized using a method of series reduction fuzzy rule base. The simulation results of a nonlinear system show that the performance of a Fed PID Hybrid Takagi-Sugeno fuzzy controller is better than that of the conventional fuzzy PID controller or Hybrid Mamdani fuzzy Fed PID controller, especially using the reduction of the number of fired rules.