Parametric identification of parallel Wiener-Hammerstein systems

TL;DR

This paper introduces a data-driven method for identifying parallel Wiener-Hammerstein systems, decomposing their dynamics into orthogonal branches and estimating static nonlinearities, validated on real-world measurements.

Contribution

It presents a novel initialization procedure for identifying parallel Wiener-Hammerstein models solely from input-output data, including decomposition and nonlinear estimation.

Findings

Effective decomposition of parallel branches demonstrated

Method validated on real-world measurement data

Proven consistency of the initialization procedure

Abstract

Block-oriented nonlinear models are popular in nonlinear modeling because of their advantages to be quite simple to understand and easy to use. To increase the flexibility of single branch block-oriented models, such as Hammerstein, Wiener, and Wiener-Hammerstein models, parallel block-oriented models can be considered. This paper presents a method to identify parallel Wiener-Hammerstein systems starting from input-output data only. In the first step, the best linear approximation is estimated for different input excitation levels. In the second step, the dynamics are decomposed over a number of parallel orthogonal branches. Next, the dynamics of each branch are partitioned into a linear time invariant subsystem at the input and a linear time invariant subsystem at the output. This is repeated for each branch of the model. The static nonlinear part of the model is also estimated during this step. The consistency of the proposed initialization procedure is proven. The method is validated on real-world measurements using a custom built parallel Wiener-Hammerstein test system.