Auto-tuning of PI Smith predictor controllers using genetic algorithms

The technique of genetic algorithms (GAs) is proposed as a means of auto-tuning PI Smith predictor controllers. The technique involves firstly using online data and the GA to identify a model of the process. Then the identified model, the GA and simulation methods, are used to off-line tune the PI Smith predictor controller, so as to minimise a time-domain based cost function. Finally, the genetically tuned controller is implemented online on the real process. The results of the auto-tuning of the PI Smith predictor controller technique are illustrated on a laboratory heat exchanger, and a comparison between this technique, and both the genetic PI auto-tuning technique and the Astrom PI auto-tuning technique are made.