Optimal Tuning of PID Parameters Using Iterative Learning Approach

Abstract

PID is the most predominant industrial controller that constitutes more than 90% feedback loops. Time domain performance of PID, including overshoot, settling time and rise time, is directly relevant to the tuning of PID parameters. In this work we propose an optimal tuning method for PID by means of iterative learning. PID parameters will be updated whenever the same control task is repeated. A novel property of the new tuning method is that the time domain performance can be incorporated directly into the objective function to be minimized. Another novel property is that the optimal tuning does not require as much the process model knowledge as other PID tuning methods. The new tuning method is essentially applicable to any processes that are stabilizable by the PID controller.