Nonlinear system identification with multiple and correlated scheduling variables

Abstract

This paper is concerned with identification of nonlinear systems with multiple and correlated scheduling variables. Multiple auto regressive exogenous (ARX) models are identified on different process operating conditions, and a normalized exponential function as the probability density function associated with each of the local ARX models taking effect is then used to combine all the local models to represent the complete dynamics of a nonlinear system. The parameters of the local ARX models and the exponential functions are estimated simultaneously under the framework of the expectation maximization (EM) algorithm. A numerical example is applied to demonstrate the proposed identification method.