Genetic algorithm based system identification and PID tuning for optimum adaptive control

In this paper the genetic algorithm optimization technique, is successfully applied in system identification and PID tuning for optimum adaptive control. In the proposed approach, two independent genetic algorithms were used sequentially. The first one is used for system model identification and the second one for PID controller tuning. Once the plant model was identified the parameters found are used to tune the PID controller. The performance of the system for a first order plant whose dynamic characteristics changes in time are presented. The results show the cascaded genetic algorithms system capability to adapt the controller to dynamic plant characteristics changes in order to increase system performance and reliability. The comparison to the conventional Ziegler-Nichols method shows the GA based system superiority