Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Modern Control Systems
Numerical recipes in Pascal: the art of scientific computing
Numerical recipes in Pascal: the art of scientific computing
Computer-controlled systems: theory and design (2nd ed.)
Computer-controlled systems: theory and design (2nd ed.)
Proceedings of the third international conference on Genetic algorithms
Sizing populations for serial and parallel genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Genetic algorithm for inducing control rules for a dynamic system
Proceedings of the third international conference on Genetic algorithms
Optimization of steiner trees using genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Computer Control of Machines and Processes
Computer Control of Machines and Processes
Genetic Algorithms and Rules Learning in Dynamic System Control
Proceedings of the 1st International Conference on Genetic Algorithms
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This paper presents a method to optimize proportional-integral-derivative (PID) control parameters, given a discrete model of the controlled process. This method is based on Holland's genetic algorithm (GA). It does not require a mathematical model of the controller to represent its dynamic behavior. It gives a solution that is not only optimal but also meets engineering constraints. Genetic algorithms do a global search without derivatives for points in a multi-dimensional search space. This method works for non-linear as well as linear systems. The objective function of the GA is based on the integrated product of time and absolute error (ITAE). The performance of the GA is compared to that of the other optimization methods. The results show that it is simple and effective.