A design algorithm using external perturbation to improve Iterative Feedback Tuning convergence

Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of sufficient process insight. It is a purely data driven approach to optimization of the loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient, which is used in a search algorithm for minimizing the performance cost. A slow rate of convergence of the tuning method is often experienced when tuning for disturbance rejection. This is due to a poor signal to noise ratio in the process data. A method is proposed for increasing the data information content by introducing an optimal perturbation signal in the tuning algorithm. The perturbation signal design is based on a detailed analysis of the asymptotic accuracy of the tuning method. A formal algorithm for optimization of the perturbation signal spectrum when tuning for disturbance rejection is presented. Special cases where an explicit optimal design are available is discussed. The theoretical analysis is supported by a simulation example.