Online automatic tuning of a proportional integral derivative controller based on an iterative learning control approach

Abstract

A new approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on an iterative learning control (ILC) approach. The method does not require the control loop to be detached for tuning, but it requires the input of a periodic reference signal. Such a reference signal can be the natural reference signal of the control system when it is used to execute a repetitive sequence, or it can be an excitation signal purely for tuning the PID controller. A modified ILC scheme iteratively changes the control signal by adjusting the reference signal only. Once a satisfactory performance is achieved, the PID controller is then tuned by fitting the controller to yield close input and output characteristics of the ILC component. Simulation and experimental results are furnished to illustrate the effectiveness of the proposed tuning method.