The primary difficulty in the identification of Hammerstein nonlinear systems (a static memoryless nonlinear system in series with a dynamic linear system) is that the output of the nonlinear system (input to the linear system) is unknown. By employing the theory of affine projection, we propose a gradient-based adaptive Hammerstein algorithm with variable step-size which estimates the Hammerst...

This paper deals with modeling and parameter identification of nonlinear systems described by Hammerstein model having Piecewise nonlinear characteristics such as Dead-zone nonlinearity characteristic. The simultaneous use of both an easy decomposition technique and the triangular basis functions leads to a particular form of Hammerstein model. The approximation by using Triangular basis functi...

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on ...

The paper deals with the parameter identification of Hammerstein systems having piecewise-linear nonlinearities with asymmetric dead-zones. A new form of nonlinearity representation provides a special form of Hammerstein model. Parameter estimation is carried out iteratively with measured input and output data records and estimated internal variables. To demonstrate the feasibility of the ident...

Abstract In this chapter we review some basic ideas for nonlinear system identification. This is a complex area with vast and rich literature. One reason the richness that very many parameterizations of unknown have been suggested, each various proposed estimation methods. We will first describe details nonparametric techniques based on Reproducing Kernel Hilbert Space theory Gaussian regressio...