Partial least squares regression for recursive system identification

Industrial processes usually involve a large number of variables, many of which vary in a correlated manner. To identify a process model which has correlated variables, an ordinary least squares approach demonstrates ill-conditioned problem and the resulting model is sensitive to changes in sampled data. In this paper, a recursive partial least squares (PLS) regression is used for online system identification and circumventing the ill-conditioned problem. The partial least squares method is used to remove the correlation by projecting the original variable space to an orthogonal latent space. Application of the proposed algorithm to a chemical processing modeling problem is discussed.>