An iterative parametric estimation method for Hammerstein large-scale systems: a simulation study of hydraulic process

This paper aims at developing an iterative method which permits to estimate the parameters of single‐input single‐output (SISO) large‐scale nonlinear systems, described by Hammerstein mathematical models. We particularly focus on the dynamic large‐scale nonlinear systems, which are made up of several interconnected nonlinear monovariable subsystems. Each subsystem can operate in a stochastic environment and be described by a discrete‐time Hammerstein mathematical model with known structure variables (order, delay) and unknown time‐varying parameters. The problem formulation is achieved based on the prediction error method and the least‐squares techniques. The convergence analysis of the recursive algorithm is provided using the differential equation approach and its performance is illustrated by treating two simulation examples.