Adaptive fuzzy PID controller based on online LSSVR

Abstract

In this paper, a predictive adaptation method based on Online Least Square Support Vector Regression (OLSVR) for a fuzzy PID controller has been proposed. Online LSSVR model is utilized to approximate the system jacobian needed to tune controller parameters. The scaling coefficients of the controller have been tuned depending on K-step ahead future behavior of the system to provide adaptation ability to the controller under changing conditions. Controller parameters are updated using Levenberg Marquard algorithm. The purpose of this paper is to improve the control performance attained by adaptive fuzzy PID which is designed based on the Jacobian information computed by the OLSSVR. The proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR), and the results show that the control performance has been improved.