Engineering and Technology Journal

Presently used modern techniques to control instead of the traditional control techniques for many industrial applications virtually or theoretically. In this research, a Fuzzy logic has been applied to control the important variables of steam generator in AL Dura station in Baghdad that generate (160 MW). These controlled variables are pressure, temperature ,air fuel ratio ,lower water level, flame and gas . Fuzzy requires a data, which obtained from actual power plant. This work explains the control on two stages during operation boiler and after operation boiler in order to make a right decision if any faults occurred during these stages at limit conditions based on fuzzy system. It was used simulation of Fuzzy system in MATLAB program. The results showed that the adoption of control technique that based on Fuzzy logic have a high response to indicate the control signals and thus can be depended as an active control system for selecting a right decisions compared with traditional systems. The adoption of fuzzy logic in control system gave the ability to take on decision for control signals with a high stability compared with traditional methods. The fuzzy system contributed in giving prophesies for station case to tell the operator what to do therefore enhancement the performance of station through to take a right decision to avoid stopping. The adoption of mfs in term (trapezoidal) showed a big corresponds with the proposed system and that is through a rapid response. The enhancement of efficiency was 10% when using fuzzy system. The accuracy of fuzzy in control was 0.99 that gave the ability to take on decision for control signals with a high stability compared with traditional methods.

This paper suggests an intelligent controller to control the manipulator movement in an environment of two and three – dimensional. The fuzzy logic controller of planning structure locally approach constructs of multi-unit. The aim is to transmit or guide the manipulator from the elected to a desired configuration. Modeling, scenarios and simulations are presented clearly in two dimensions and three dimensions together with their analysis which be done using MATLAB software. In addition, the results of the robot navigation in two-dimensional environments also compared with the results of the navigation in three-dimensional environments to clarify the strength of the suggested intelligent controller, where results (in rad) for the third link for both two and three- dimensional environments are minimum: 1.9548×〖10〗^(-4) and -7.452147499×〖10〗^(-4) in the scenario 1 also minimum results in scenario 2 as the following: -0.0061 and -0.0018. Simulation results indicate this manipulator successfully reached the desired goal configuration.

This paper presents an application of highbrid controller of a Separately Exited DC Motor (SEDM) using PSO-FL techniques. The controller is designed depending on fuzzy logic rules are such that the systems are fundamentally robust. These rules have capability learning, can learn and tune rapidly, even if the motor parameters are varied. But, adapting fuzzy controller parameters is very complex and depends on operator experience. Therefore a Particle Swarm Optimization technique was adapted for obtaining the centers and the width of triangle inputs membership functions. The FL method is represented too. The complete mathematical model and simulation of a separately excited dc motor is represented using MATLAB10a/SIMULINK. The simulation results demonstrate that the proposed PSO-FL speed controller realizes a good dynamic behavior of the SEDM with very good speed tracking.

In this paper, a study on tuning of fuzzy logic controller (FLC) using genetic algorithm (GA) for controlling an armature controlled DC motor as an example of linear plant and for controlling nonlinear plant as another example is performed. There are different ways in which a FLC can be tuned, like: tuning the scaling gains, Rule Base (RB), and Data Base (DB) represented by type of membership functions or parameters of membership functions used. The tuning process in this paper includes a multi-stage tuning represented by searching the good scaling gains, RB, and DB then a combination of multi-stage (CMS) tuning methods using Genetic Algorithm (GA) based on a fitness function that is defined in terms of performance criterion (Integral of Squared Error ISE). The performances of these tuning stages are evaluated and a comparison between them has been introduced using linear and nonlinear plants.

This paper presents an application of Fuzzy Logic Control (FLC) in the separately excited Direct Current (DC) motor drive (SEDM) system; the controller designed according to Fuzzy Logic rules. Such that the system is fundamentally robust. These rules have capability learning, can learn and tune rapidly, even if the motor parameters are varied. The most commonly used method for the speed control of dc motor is Proportional- Integral- Derivative (PID) controller. Simulation results demonstrate that, the control algorithms Neuro-Fuzzy logic and PID, the dynamic characteristics of the SEDM (speed, torque, as well as currents) are easily observed and analyzed by the developed model. In comparison between the Neuro-fuzzy logic controller and PID controller, the FLC controller obtains better dynamic behavior and superior performance of the DC motor as well as perfect speed tracking with no overshoot, and the proposed controller provides high performance dynamic characteristics and is robust with regard to change of motor speed and external load disturbance. This paper also discusses and compares the speed control systems of SEDM using PID- controller conventional and Fuzzy Logic-controller. The entire system has been modeled using MATLAB 10a/SIMULINK toolbox.

In the present work the dynamic behavior of a plate heat exchanger (PHE)
(single pass counter current consists of 24 plates) studied experimentally and
theoretically to control the system. Different control strategies; conventional
feedback control, classical fuzzy logic control, artificial neural network (NARMAL2)
control and PID fuzzy logic control were implemented to control the outlet
cold water temperature. A step change was carried in the hot water flow rate which
was considered as a manipulated variable. The experimental heat transfer
measurements of the PHE showed that the overall heat transfer coefficient (U) is
related to the hot water flow rate (mh) by a correlation having the form:
U mh
0.7158 =11045
In this work the PHE model was found theoretically as a first order lead and
second order overdamped lag while the experimental PHE represented dynamically
(by PRC method) as a first order with negligible dead time value. A comparison
between the experimental and the theoretical model is carried out and good
agreement is obtained. The performance criteria used for different control modes
are the integral square error (ISE) and integral time-weighted absolute error (ITAE)
where the ITAE gave better performance. As well as the parameters of the step
performance of the system such as overshoot value, settling time and rise time are
used to evaluate the performance of different control strategies. The PID fuzzy
controller gave better control results of temperature rather than PI, PID and
artificial neural network controller since PID fuzzy controller combines the
advantages of a fuzzy logic controller and a PID controller. MATLAB program
version 7.10 was used as a tool of simulation for all the studies mentioned in this
work.

Model uncertainty and robustness have been a central theme in the field of automatic control. Many control techniques are used to reduce the effects of uncertainty which may appear in different forms as disturbances, dynamic delays or as other imperfections in the models used.In this paper a comparison between conventional type -1 fuzzy logic controller and type -2 fuzzy logic controller has done in simulation conditions of a simple temperature control of a furnace system to show the great effect of the new generation of fuzzy logic
controllers to improve the performance of a system with high level of uncertainty.