Optimization of PID controllers using ant colony and genetic algorithms (Q714914)

``This book describes a real time control algorithm using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm for optimizing PID controller parameters. Proposed method is tested on GUNT RT 532 Pressure Process Control System. The dynamic model of the process to be controlled is obtained using Artificial Neural Network (ANN) theory. Using the chosen model, the parameters of PID controller are optimized with ACO, GA and Ziegler-Nichols (ZN) techinques. The performances of these three techniques are compared with each other using the criteria of overshoot, rise time, settling time and root mean square (RMS) error of the trajectory. It is observed that the performances of GA and ACO are better than that of ZN technique''. (From the Foreword of the book)