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Engineering - Computational Intelligence and Complexity | Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

Ünal, M., Ak, A., Topuz, V., Erdal, H.

2013, XX, 88 p.

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  • Novel optimization methods for process system control
  • A novel real time control algorithm, that uses Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm for optimizing PID controller parameters
  • Artificial neural networks for modelling complex and non-linear systems

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.

Content Level » Research

Keywords » Ant Colony Optimization Algorithms - Artificial Intelligent - Artificial Neural Network - Genetic Algorithms - Modelling - PID Controller - Process System Control - Real Time Control - Ziegler-Nichols Method

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity - Control Engineering

Table of contents 

Artificial Neural Networks.- Genetic Algorithm.- Ant Colony Optimization (ACO).- An Application for Process System Control.

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