System Identification Of Internal Model Control

Despite the fact that PID controllers are undoubtedly the most popular controller used in industrial control processes for decades, they do not perform well when applied to systems with significant time-delay. Recently , researchers have realized that the IMC is particularly good for controlling time-delayed plants. Using of two algorithms Least square & Genetic algorithm designed the new internal model controller , the issue of designing a internal model controller for dealing with the parameter uncertainties existing in the plant. The genetic algorithm controller design technique for dealing with parameter uncertainty existing in the plant will then be deployed to design internal model controllers. In particular the objective of the thesis are to: Investigate the deployment of the genetic algorithm in designing system identification for the plant. Developed simplified approach in designing IMC . Investigate the deployment of the genetic algorithm in designing IMC controller. Compare the performance of genetically tuned controller with Least square . Implement the genetically & least square tuned IMC controller on a real system and compare the performanc