In this paper Adaptive-Network-Based Fuzzy Inference System (ANFIS) architecture is presented and has been used as a tool for System Identification. System Identification consists of three related steps: 1:) Structure specification, 2:) Parameter estimation. 3:) Model Validation. These steps are also discussed. We use a new method to determine the structure of the ANFIS model (Fuzzy Curves). Fuzzy Curves Help in determining the number of significant inputs from a number of candidates. We determine the number of membership functions in each input by using the subtractive clustering. Finally this method is tested on two cases.

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Development of new energy resources has become an important object. The proposed of separating hydrogen form water for utilization as a substitute for oil and other fossil fules has become a field of interest for energy planners and decisions makers. The present work is concerned with studying and analyzsng a photovoltaic (Poly-Si manufactured by Solarex co) solar hydrogen system installed over the roof the institute of Astronomty and Genphysics at Helwan. Egypt. Fig (1): The system is constructed to measure the temperature of solar cell at center of the panel and near its edges. Using two thermocouples as sensors. The ambient air temperature was measured simultaneously with that of solar cells for comparison. The normal incident beam of global solar radiation is recorded by a doom sensor, which was installed in a parallel position. Also, measurements of the environmental and meteorological effects are performed every half hour, electrical parameters, were carried out before the noon lime to sunset with variance in the solar radiation. The power output of solar cells. The amount of energy contained in sunlight just outside the earth's atmosphere is about 1.4km/m2. In actual use, solar cells convert between 5 and 20%of incident solar energy into electric energy. depending upon the specific solar cell construction and prevailing operating conditions.

A control design technique based on the theory of variable structure (VSS) is proposed. The proposed technique is developed for designing an optimal model following adaptive controller, for an electromechanical system. The model that specifies the design objectives is a part of the system. A systematic procedure for the selection of the switching hyperplanes in the design of the optimal variable structure controller is developed, by minimizing a quadratic performance index in the sliding mode operation. The design procedures describes and the results on a simulation study is presented, showing the effectiveness of the designed controller under the effect of disturbances.

Robotic systems represent one of the most advanced industrial applications. Such robotic systems have a severe oscillatory behavior, thus the conventional control techniques are not suitable to deal with it. In the present paper, the dynamic dynamic behavior of robotic systems is studied and a dynamic model for such systems is obtained. A suitable trajectory for each link is pre-determined. The technique introduced proposes a segmented trajectory rather than a continuous one. A comparative study between the behaviour of the system with the proposed technique and that proposed in [7] is introduced. The above mentioned algorithm assumed that the chosen path is continous and this can be used in some tasks, required to be carried-out by the robot. But in other tasks, it is sometimes required that the robot must move a short distance from a point to another neighbouring point i.e. , the path is not continuous but, it is a segmented path. Such pathes are required in different tasks, such pathes are weiding, paint spraying, cxarrying a tray of drink……..etc.

An algorithm for calculating the apparent transmission line impedance to the point of fault is presented. This algorithm is based on a half-cycle Window using walsh function technique. The real and the imaginary components of the fundamental frequency voltage and current of the faulty phases are evaluated and used to calculate the impedance, as seen from the relay location. The sampling rate used to test this algorithm is 16 samples/cycle. The algorithm has been tested using digital computer simulation of the sample model. A comparison between this algorithm and four other algorithms, suitable for fault impedance measurement was carried on here with particular reference to their accuracy and speed of calculations.

In the present p ap er, a new algorithm for solving the control law in electrical power systems, is introduced. This new algorithm is based on the sliding mode prop erty, existing in variable structure systems (VSS). The resulting control law is discontinuous by its nature. However, it does not require informations about the system parameters or its manner of variation. The only requirements are the maximum and minimum limits of variation for each p arameter. An adap tive controller is designed, for an electrical p ower system, on the basis of that new algorithm. The main function of the controller 18 the adap tive control of the electrical power system, Meanwhile, it can be used as an observer Theoretical and comp utetional results, using that controller insure the adaptive control p roperty in the electrical power system model. The adaptive prop erty verifies under rapid and wide range of parameters variation and also under the effect of a unit step external disturbance.

The conventional PID controller has static parameters that cannot be changed at different operating conditions. As a result, the term ‘adaptive PID controller’ has appeared to solve this problem. This controller can be tuned using intelligent techniques such as Fuzzy Logic Control, Neural Network Control, or Adaptive Neuro-Fuzzy Inference Systems. However, the choice of the suitable parameters for these intelligent controllers has a direct effect on their performance. Metaheuristics algorithms—with their powerful performance, speed, and optimal parameter selection—can be applied for choosing controller parameters efficiently. In this paper, a hybrid of genetic algorithm and particle swarm optimization is proposed to tune the parameters of different adaptive PID controllers. To evaluate the performance of the proposed hybrid optimization method on the different adaptive PID controllers, these controllers are applied to control the operation of one of the most difficult chemical processes, the divided wall distillation column. The proposed column used in this work separates a ternary mixture of ethanol, propanol, and n-butanol. Our proposed hybrid optimization technique is compared with the genetic algorithm, and simulation results show that our proposed hybrid genetic-particle swarm technique outperforms genetic algorithm for different disturbances.

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In this article a direct torque of the induction motor drive controlled by Neuro-Fuzzy systet. The proposed control scheme uses the stator flux amplitude and the electromagnetic torque errors through an adaptive Neuro_fazzy inference system (ANFIS) to act on both the amplitude and the angle of the desired reference voltage. Simulation results by using ANFIS are compared with those of the conventional direct torque control (DTC). The comparison results of Direct torque Neuro-fuzzy controller (DTNFC), illustrate the reduction in the torque and stator flux ripples. The validity of the proposed method is confirmed by the simulative results

Traffic congestion is an important socio-economic problem that swelled in the last few decades. It affects the social mobility of people, length of trips, quality of life, and the economy of countries. As a major problem in most countries, it has been tackled by governments, universities, and advanced research using intelligent transportation systems (ITS) to solve the problem or at least ease its adverse effects. Hidden Markov Models (HMM) represent one of the methods that are suitable for congestion prediction. In this paper, a new model, based on Hidden Markov Model and Contrast, is proposed to define the traffic states during peak hours in two dimensional space (2D). The proposed model uses mean speed and contrast to capture the variability in traffic patterns. Empirical evaluation shows that the proposed approach has improved prediction error in comparison to HMM related work and neuro-fuzzy approaches.

In this article we propose two approaches to improve the direct torque control (DTC) of an aduction motor (IM) such as fuzzy logic (FL) and artificial neural network (ANN), applied in witching select voltage vector. Slaulation results of two approaches compared with those of muveantional direct torque control (DTC). The comparison results of the (FL_DTC) and (ANN DTC) illustrate the reduction in the torque and stator flux ripples. The validity of the proposed methods is confirmed by the simulative results. The two approaches are explained in clear details which are designed using SIMULINK vader Matlab Ver.7.7 software package. Also, MATLAB2008/FUZZY toolbox is used to implement the fuzzy logic coptroller. Both systems are lawlated under the same conditions

Traffic Congestion is a socio-economic problem that swelled in the past few decades.Intelligent Transportation Systems (ITS) has become the cutting edge solution to most traffic problems.One of the important problems is the prediction of the incoming traffic pattern.There are a number of available approaches for traffic congestion prediction.One approach using NeuroFuzzy is discussed here.The approach is modified into a hybrid one using Hidden Markov Models (HMM).HMM is implemented to take into consideration time factor.It is used to select the right NeuroFuzzy network suitable for this particular time period for efficient congestion prediction.The novelty in this research is: 1) showing that the right choice of traffic pattern for training affects the quality of the prediction dramatically.2) The results from the hybrid model showing 6% MAE rate which outperforms the standard standalone NeuroFuzzy approach of 15% error.

Distillation columns have proved to be the most reliable separation method for separating chemical mixtures to their pure components. As the number of components to be separated increases, the number of columns and the energy required to run these columns also increase. This results in huge capital costs that are unaffordable due to limited energy resources. A most "compact" column called the divided wall column appeared to solve this difficulty. This column is capable of separating mixtures of three or more components to high purity products with energy less than that of the conventional multicolumn process. However, the control of these columns is more complicated due to the coupling effect and increased number of variables in these columns. Our focus in this paper is limited to the divided wall columns separating ternary mixtures only. There are three main objectives of this paper. First, the paper presents a survey study about the different control aspects of the divided wall columns, based on the type of control used: composition control, temperature control, or cascaded composition-temperature control. An up-to-date overview of most of the current literature is presented. Second, conventional and adaptive proportional-integral-derivative (PID) controllers are proposed to control a divided wall distillation column separating a ternary mixture of ethanol, propanol, and n-butanol. Fuzzy logic control, neural network control, and adaptive neuro-fuzzy inference systems are suggested for the tuning of the PID controllers. Particle swarm optimization technique is also applied to improve the results obtained by the adaptive PID controllers. Finally, a multi-input-multi-output neural network model reference adaptive controller based on adaptive PID controller tuned by adaptive neuro-fuzzy inference systems based particle swarm optimization is suggested. The results indicate the superiority of the adaptive PID controllers over conventional PID controllers, especially in the case of disturbances.

This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the second is Adaptive Neural Network Model Predictive Control, and the third is Adaptive neuro-fuzzy sliding mode controller. These methods are applied to a multivariable nonlinear system as an ammonia–urea reactor system. The main target of these controllers is to achieve stabilization of the outlet concentration of ammonia and urea, a stable reaction rate, an increase in the conversion of carbon monoxide(CO) into carbon dioxide(CO 2 ) to reduce the pollution effect, and an increase in the ammonia and urea productions, keeping the NH 3 /CO 2 ratio equal to 3 to reduce the unreacted CO 2 and NH 3 , and the two reactors’ temperature in the suitable operating ranges due to the change in reactor parameters or external disturbance. Simulation results of the three controllers are compared. Comparative analysis proves the effectiveness of the suggested Adaptive neurofuzzy sliding mode controller than the two other controllers according to external disturbance and the change of parameters. Moreover, the suggested methods when compared with other controllers in the literature show great success in overcoming the external disturbance and the change of parameters.

Traffic Congestion is a complex dilemma facing most major cities. It has undergone a lot of research since the early 80s in an attempt to predict traffic in the short-term. Recently, Intelligent Transportation Systems (ITS) became an integral part of traffic research which helped in modeling and forecasting traffic conditions. In this paper, two frameworks for traffic congestion prediction are proposed. The first framework is based on NeuroFuzzy model which is well surveyed in traffic literature. The second framework is based on Hidden Markov Models (HMM) which is rarely used in traffic prediction. The methods are used to define traffic congestion during morning rush hours. The results of the two methods are compared.

Genetic algorithms (GAs) have been fairly successful in a diverse range of optimization problems, providing an efficient and robust way for guiding a search even in a complex system and in the absence of domain knowledge. This paper, presents a comparative study for a chemical reactor system by conventional P, PI, PID, and by using genetic algorithm for the same plant. The results obtained here, assure the actual possibility of using GA to identify and controlling plants. The major efforts are to adjust the controller in order to minimize the steady state error. GA offer, an alternative approach both for identification and control of nonlinear processes in process engineering. Keywords— genetic algorithm, GA, PID Controllers, Stochastic systems, chemical reactor, optimization.