The identification of nonlinear models for process control using tailored “plant-friendly” input sequences

This paper considers certain practical aspects of the identi®cation of nonlinear empirical models for chemical process dynamics. The primary focus is the identi®cation of second-order Volterra models using input sequences that o€er the following three advantages: (1) they are ``plant friendly;ʹʹ (2) they simplify the required computations; (3) they can emphasize certain model parameters over others. To provide a quantitative basis for discussing the ®rst of these advantages, this paper de®nes a friendliness index f that relates to the number of changes that occur in the sequence. For convenience, this paper also considers an additional nonlinear model structure: the Volterra±Laguerre model. To illustrate the practical utility of the input sequences considered here, second- order Volterra and Volterra±Laguerre models are developed that approximate the dynamics of a ®rst-principles model of methyl methacrylate polymerization.