System Identification using Orthonormal Basis Filters

The widely used dynamic models for identification of linear time invariant systems in process industries are Auto Regressive with Exogenous Input (ARX) and Finite Impulse Response (FIR) models. Their popularity is due to their simplicity in developing the model. However, they need very large amount of data to reduce variance error, in addition ordinary ARX model structures lead to inconsistent model parameters. Orthonormal Basis Filter (OBF) model structures permit incorporation of prior knowledge of the system in the form of one or more poles, which renders it the capacity to capture the system dynamics with a few number of parameters (parsimonious in parameters). In addition, the resulting OBF models are consistent in parameters. The model parameters can be easily developed using linear least square method. In this study, OBF model development for simulation and real case studies is presented.