EN-PH: a regularization-based model predictive control relevant identification method

Abstract

Control-Relevant Identification (CRI) proposes to generate models that are suitable to the control problem at hand, creating synergy between the control and identification algorithms. For model predictive control (MPC), it is recommended to use a model obtained by the minimization of a multi-step ahead prediction error cost function. One way of minimizing the cost function is applying the Levemberg-Marquardt (LM) optimization algorithm, provided the identification data set is not ill-conditioned. A second and more interesting approach is the line search method known as PLS-PH. In this work, a regularization-based line search method denoted as EN-PH is proposed to perform the minimization of the identification cost function. Two examples are presented to compare the predictive performance of models obtained using Least Squares, LM, PLS-PH and EN-PH. The examples have shown that the models fitted with EN-PH outperform the other models.