A Recursive Method of Nonlinear System Identification Using Block-oriented Models

Abstract The paper deals with recursive identification of nonlinear dynamic systems using block-oriented models. The models consist of various combinations of linear dynamic systems and no-memory nonlinear blocks. Special forms of block-oriented models are presented based on the separation of key terms in general block descriptions. Incorporating of these models into a recursive identification scheme as adjustable models is discussed. The resulting adjustable nonlinear models are linear in all the parameters. This enables simultaneous estimation both the parameters of linear pulse transfer functions and the coefficients of polynomials approximating nonlinear characteristics. The method of recursive least-squares estimation is applied and is extended including sensitivity coefficients.