Vedat Topuz

Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.

Modeling of dimmable High Power LED illumination distribution using ANFIS on the isolated area

High power light emitting diodes (HP-LEDs) are more suitable for energy saving applications and have becoming replacing traditional fluorescent and incandescent bulbs for its energy efficient. Therefore, HP-LED lighting has been regarded in the next-generation lighting. In this study, illumination distribution of white color HP-LED is modeled by adaptive neuro-fuzzy inference system (ANFIS) approach on the isolated area while LED head is fixed. Subtractive clustering with hybrid learning approach is used to train the realized ANFIS architectures. End of the numerous experiment we finally concluded that, ANFIS could be used to modeling the illumination distribution applications perfectly.

Trajectory tracking performance comparison between genetic algorithm and ant colony optimization for PID controller tuning on pressure process

The main goal of this study was to compare the performances of genetic algorithm (GA) and ant colony optimization (ACO) algorithm for PID controller tuning on a pressure control process. GA and ACO were used for tuning of the PID controller when predefined trajectory reference signal was applied. Offline learning approach was employed in both GA and ACO algorithms. Realized pressure process dynamic has nonlinear behavior, thus system was modeled by nonlinear auto regressive and exogenous input (NARX) type artificial neural network (ANN) approach. PID controller was also tuned by Ziegler–Nichols (Z–N) method to compare the results. A cost function was design to minimize the error along the defined cubic trajectory for the GA‐PID and ACO‐PID controller. Then PID controller parameters (Kp, Ki, Kd) were found by GA‐PID, ACO‐PID algorithms, which were adjusted with their optimal parameters. It was concluded that both ACO and GA algorithms could be used to tune the PID controllers in the pressure process with excellent performance. This material is suitable for an engineering course on neural networks, genetic algorithm, ant colony optimization and process control laboratory.

Ant Colony Optimization (ACO)

The ant colony optimization algorithm (ACO) is an evolutionary meta-heuristic algorithm based on a graph representation that has been applied successfully to solve various hard combinatorial optimization problems. Initially proposed by Marco Dorigo in 1992 in his PhD thesis [49], the main idea of ACO is to model the problem as the search for a minimum cost path in a graph. Artificial ants walk through this graph, looking for good paths. Each ant has a rather simple behavior so that it will typically only find rather poor-quality paths on its own. Better paths are found as the emergent result of the global cooperation among ants in the colony [13, 15, 50-52].

Soft computing technique for power control of Triga Mark-II reactor

In this study, a trajectory tracking fuzzy genetic controller for Istanbul Technical University Triga Mark-II nuclear research reactor design approach is given. Power output of reactor is controlled along the predefined trajectory by fuzzy logic controller. Designed zero order Sugeno type fuzzy logic controller membership boundary value and rule weights are found by genetic algorithm. Non-chattering control with smooth control surface is also achieved using constrained fitness functions. Simulation results shows that reactor power successfully tracks the given trajectory under various working conditions and reaches the desired power level within the determined period within small tracking error.

Traffic demand prediction using ANN simulator

The prediction of the traffic data is a vital requirement for advanced traffic management and traffic information systems, which aim to influence the traveler behaviors, reducing the traffic congestion, improving the mobility and enhancing the air quality. Both the stochastic time series (TS) techniques and artificial intelligent (AI) techniques can be used for this aim. Daily traffic demand in Second Tolled Bridge of Bosphorus, which has an important role in urban traffic networks of Istanbul has been predicted by both a TS approach using an autoregressive (AR) model, and an AI approach using an artificial neural network (ANN) model. The results have shown that the prediction error obtained by ANN model is smaller than the error obtained by AR model. The results have also pointed out that many other transportation data prediction studies can be implemented easily and successfully by using the developed ANN simulator.

Recognition of turkish vowels by probabilistic neural networks using yule-walker AR method

In this work, recognition of vowels in Turkish Language by probabilistic neural networks is implemented using a spectral analysis method Power spectral density of the phones obtained from speakers is estimated Then weighted power spectrum is calculated after power spectral density of that phone is passed through a number of band pass filters In this way, estimated power spectrums of the phones which are obtained from speakers approximate to a mel scale Mel scale coefficients obtained, form the feature vector of the phone that is pronounced These feature vectors constitute the database of the related speaker Thus and so, every speaker has its own database When it comes to recognize a phone pronounced by a speaker later, a probabilistic neural network model is created using the database belonging to that speaker The feature vector of the phone which is to be recognized is computed as mentioned above In this study, speaker-dependent recognition of Turkish vowels has been realized with an accuracy rate of over 95 percent.

Modeling of high power LED illumination distribution using ANN

High power light emitting diodes (HP-LEDs) are more suitable for energy saving applications and have becoming replacing traditional fluorescent and incandescent bulbs for its energy efficient. Therefore, HP-LED lighting has been regarded in the next-generation lighting. In this study, illumination distribution of white color HP-LED was examined and modeled by artificial neural network (ANN) to use at the different lighting applications. Illuminance measurements were done at different distances and voltage levels in the isolated test room. The obtained data was used to model the HP-LED illuminance distribution by ANN. As the realized ANN model, it was presented illumination distribution graphs. Matlabs neural network tool box was used for the simulations.

Field Programmable Gate Arrays Based Real Time Robot Arm Inverse Kinematic Calculations and Visual Servoing

DOI: 10.26650/electrica.2018.49877 Reliability and precision are very important in space, medical, and industrial robot control applications. Recently, researchers have tried to increase the reliability and precision of the robot control implementations. High precision calculation of inverse kinematic, color based object recognition, and parallel robot control based on field programmable gate arrays (FPGA) are combined in the proposed system. The precision of the inverse kinematic solution is improved using the coordinate rotation digital computer (CORDIC) algorithm based on double precision floating point number format. Red, green, and blue (RGB) color space is converted to hue saturation value (HSV) color space, which is more convenient for recognizing the object in different illuminations. Moreover, to realize a smooth operation of the robot arm, a parallel pulse width modulation (PWM) generator is designed. All applications are simulated, synthesized, and loaded in a single FPGA chip, so that the reliability requirement is met. The proposed method was tested with different objects, and the results prove that the proposed inverse kinematic calculations have high precision and the color based object recognition is quite successful in finding coordinates of the objects.

An Application for Process System Control

The purpose method is implemented to stabilize the pressure of the tank at the desired pressure level adjusting the input air flow despite the continuous exhaust output flowing as a disturbance. Because of compressibility of air and nonlinear characteristic of valves, realized system has nonlinear dynamics. Cubic trajectory function was used as an input reference signal, to prevent the pressure fluctuations and large overshoot in tank which could be harmful in some process [56].