Identification and Control of Nonlinear Systems Using Fuzzy Hammerstein Models

Abstract

This paper addresses the identification and control of nonlinear systems by means of Fuzzy Hammerstein (FH) models, which consist of a static fuzzy model connected in series with a linear dynamic model. For the identification of nonlinear dynamic systems with the proposed FH models, two methods are proposed. The first one is an alternating optimization algorithm that iteratively refines the estimate of the linear dynamics and the parameters of the static fuzzy model. The second method estimates the parameters of the nonlinear static model and of the linear dynamic model simultaneously by using a constrained recursive least-squares algorithm. The obtained FH model is incorporated in a model-based predictive control scheme and a new constraint-handling method is presented. A simulated water-heater process is used as an illustrative example. A comparison with an affine neural network and a linear model is given. Simulation results show that the proposed FH modeling approach is useful for modular parsimonious modeling and model-based control of nonlinear systems.

Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models

“…Bearing in mind all aforementioned disadvantages of polynomial Hammerstein model, it is obvious that different structures have been used as the steady-state part of the model, for example polynomial splines [10], fuzzy systems [1], Support Vector Machines (SVM) [14] and wavelets [45]. An interesting alternative is to use neural networks [2,25].…”

A New Identification Approach of MIMO Hammerstein Model with Separate Nonlinearities

“…Serval approches have been proposed to identify MIMO Hammerstein model. In [15,16,17], neuronal networks and fuzzy logic have been used to deal with more general nonlinearities. An approach based on multivariable cardinal cubic spline functions to model the static nonlinearities have been proposed in [18].…”

Identification of MIMO Hammerstein models using Singular Value Decomposition approach

“…AlDuwaish et al in [2] used an hybrid model consisting of a neural network to identify the static nonlinear part in series with Auto-Regressive Moving Average (ARMA) model for identification of SISO and Multi-Input Multi-Output (MIMO) Hammerstein models. Several other identification and controller design methods for Hammerstein models were developed by [1,20,30].…”

“…neural networks or fuzzy logic [1,2,14,15], and polynomial with cross-terms have often been used to deal with more general nonlinearities. Recently, several approaches have been proposed to identify MIMO Hammerstein models.…”