Bilinear Identification of Nonlinear Processes

Abstract

Identification of nonlinear processes using bilinear models is presented. To avoid direct estimation of the bilinear model parameters, a dynamically equivalent input/output model structure is developed through an analysis of the observability of the bilinear system. Calculation of the Volterra kernels from the input/output model parameters is presented and the overparameterization of the input/output model form is examined by comparison with the Volterra series model form. Constraint equations calculated from this guarantees the dynamic equivalence of the input/output and state-space forms. The input/output model parameters are estimated through an extended least-squares method. An example of a fluid catalytic cracking unit (FCCU) is presented.