The Design of Frequency Filters of Iterative Feedback Tuning Using Particle Swarm Optimization

Iterative feedback tuning (IFT) is a data-based tuning approach that minimizes a quadratic performance index using some closed-loop experimental data. A control weighting coefficient, known as lambda, and two frequency filters are the most important parameters which can significantly improve the performance of the method. One of the major problems in IFT is tuning these parameters. This paper presents a new approach to tune frequency filters using particle swarm optimization (PSO). At the end, the performance of the proposed method is evaluated by two case study simulations. 1. Introduction Iterative feedback tuning (IFT) is a data-based method for the tuning of controllers with restricted complexity which was proposed by Hjalmarsson et al. [1] in 1994. In this method, the problem of model bias could be avoided by replacing the information carried by the model with information achieved directly from the system itself. This leads to an iterative method where the controller parameters are successively updated using information from closed-loop experiments with the most recent controller in the loop. IFT has proved to be very effective in practice and is now greatly used in process control [2]. Since 1994 many experiences have been achieved by IFT algorithm and lots of improvements have been observed [3–13]. In order to fully take advantage of IFT, a new scheme needs to be introduced to tune the efficient parameters. In earlier studies, the importance of IFT parameters was confirmed and a few tuning key points were presented [8], but the tuning of these parameters has not been treated in much detail. However, some approaches have been proposed to tune similar parameters. Shridhar and Cooper [14] derived an analytical expression in which the suppression coefficients are calculated as a function of the plant model parameters. Kai et al. [15] proposed a min-max algorithm to design tuning parameters. Al-Ghazzawi et al. [16] presented an approach to tuning MPC on-line based on sensitivity equations derived from a step response model with linear constraints. The IFT’s parameters are mainly a scalar, named lambda, and two frequency filters. This paper provides a method to tune the frequency filters which can emphasize or suppress specific frequency bands of the output and control signals. These filets affect the IFT’s performance greatly. Therefore, convenient design of these filters seems critical. In order to tune these filters, the particle swarm optimization (PSO) is proposed, which tunes frequency filters using the input-output data achieved from IFT