Muhammet Unal

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.

Fault diagnosis on bottle filling plant using genetic-based neural network

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA-ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA-NN environment. It may also be used for training for selection of ANN configurations at various applications.

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.

Implementation of the Surface Response to Excitation Method for Pipes

The Surface response to excitation (SuRE) method was developed to detect the defects and loading condition changes on plates without using the impedance analyzer. The SuRE method excites the surface with a piezoelectric exciter. Generally, sweep sine wave is continuously applied and surface waves are monitored with (a) piezoelectric element(s) or noncontact sensor(s). The change of the spectral characteristics is quantified by using the sum of the squares of the differences (SSD) to detect the defects. In this study, the SuRE method was implemented for detection of the defects in pipes. The surface of a pipe was excited with a continuous sweep sine wave and the dynamic response of the pipe on selected points were monitored by using a scanning laser vibrometer. The study shows that the SuRE method can be used effectively for detection of damage and estimation of its severity in pipe like structures.

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].

A review on real-time big data analysis in remote sensing applications

Thanks to the new technologies of remote sensors, remote sensor data which increases in size exponentially and transforms to big data with increasing spatial and spectral resolutions causes great difficulties in storage, transportation and processing. It has become a necessity to implement parallel approaches instead of traditional methods, which are inadequate in critical applications when real/near real-time analysis of relevant large data is needed. In this paper, high performance computing approaches such as Multi-core, FPGA, GPU, Cluster and Cloud Computing are investigated for real / near real-time large data analysis in remote sensing applications.

Load Monitoring Using Surface Response to Excitation Method

Surface response to excitation (SuRE) method is a low-cost alternative to the electromechanical impedance method developed for the structural health monitoring (SHM) applications. This method uses one piezoelectric transducer to excite the surface of a structure with a sweep sine wave. One or more piezoelectric sensors or scanning laser vibrometer monitors the dynamic response of the surface to the excitation. The spectrum of the dynamic response is collected at the optimal operating conditions. Any significant change of the spectral characteristics may indicate defects, improper loading or loose fasteners.

Cellular Neural Network training by ant colony optimization algorithm

Cellular Neural Networks (CNN) having parallel processing capabilities present important advantages in image processing applications. The coefficients of the template matrices and the threshold values of CNN should be optimized to obtain the desired output image. The learning algorithms designed for classical feed forward neural networks are not suitable for CNN due to its dynamic architecture. Researchers are still working on development of generalized learning algorithms for CNN. In this study, the CNN training is realized by ant colony optimization (ACO) technique. The results obtained by trained CNN show that ant colony based learning algorithm is very successful for image feature extraction problems such as edge, corner, vertical and horizontal edge detections.

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].

Reconstruction the complete image from random scattered pieces of image

Combining all image pieces which are randomly scattered to reconstruct the initial (original) image takes so much time. For instance, combining the all image pieces used in tiling is a tedious and time consuming task. For this purpose, the hardness of the task is able to be decreased and the task duration is able to be shortened using the developed image processing optimization algorithms. In this study, the reconstruction of initial image is implemented using image processing methods from randomly scattered image pieces. In the first stage, the edge improvement is completed to reach the initial image position from randomly scattered image pieces. Then, the rotating angles of image pieces are determined with the support of corner finding algorithm. The initial image positions of pieces are determined and positioned to their original places by individually comparing the prescribed image pieces with the initial image pieces.