Multiple model LPV approach to nonlinear process identification with EM algorithm

This paper is concerned with the identification of a nonlinear process which operates over several working points with consideration of transition dynamics between the working points. Operating point changes due to economic considerations (e.g. grade change in polymer plants) or working environment changes (e.g. feed raw materials property change) are commonly experienced in process industry. These transitions among different operating conditions excite the inherent nonlinearity of the chemical process and pose significant challenges for process modeling. To circumvent the difficulties, we propose a probability-based identification method in which a linear parameter varying (LPV) model is built using process input–output data. Without knowing the local model dynamics a priori, only excitation signals around each operating point are required to identify linear models of the local dynamics, and then the local models are synthesized with transition data to construct a global LPV model. Simulated numerical examples as well as an experiment performed on a pilot-scale heated tank are employed to demonstrate the effectiveness of the proposed method.