Perturbed Iterative Feedback Tuning

Optimal performance of process control requires a controller synthesis based on a perfor-mance criterion. In order to minimize the criterion, a model for the process is normally required. Iterative Feedback Tuning (IFT) is a data driven methodology to tune controller parameters given a performance criterion, with penalty on the controlled variable deviations from a desired trajectory and with penalty on the control variable it self or it's increment. The methodology published in [1] have been extended and tested for a number of applica-tions [2]. The method minimizes the performance criterion using a gradient based search method. The gradient is for single loop controllers estimated through up to three closed loop experiments, designed to give a consistent estimate. The rst experiment provides input/output information of the system and the remaining experiments are used to form an estimate of the gradients of the input and output with respect to the controller parameters. Using closed loop data is advantagous since it is the loop performance that is subject to optimization. It also alows tuning of a stabilizing loop. The advantage of IFT relies on the ability to achieve the desired performance in a few iterations. Plant experiments are costly and product produced meanwhile may have reduce