Cone Crusher Model Identification Using Block-Oriented Systems with Orthonormal Basis Functions

TL;DR

This paper develops a block-oriented system approach using orthonormal basis functions to accurately model cone crusher dynamics, demonstrating the superiority of Hammerstein-Wiener models with Laguerre functions over Wiener models.

Contribution

It introduces a novel application of Laguerre-based block-oriented models and adaptive recursive least squares for cone crusher system identification, highlighting the effectiveness of Hammerstein-Wiener structures.

Findings

Hammerstein-Wiener model reduces mean square error to 11%.

Wiener model is unstable and less accurate.

Hammerstein-Wiener model suitable for nonlinear predictive control.

Abstract

In this paper, block-oriented systems with linear parts based on Laguerre functions is used to approximation of a cone crusher dynamics. Adaptive recursive least squares algorithm is used to identification of Laguerre model. Various structures of Hammerstein, Wiener, Hammerstein-Wiener models are tested and the MATLAB simulation results are compared. The mean square error is used for models validation. It has been found that Hammerstein-Wiener with orthonormal basis functions improves the quality of approximation plant dynamics. The mean square error for this model is 11% on average throughout the considered range of the external disturbances amplitude. The analysis also showed that Wiener model cannot provide sufficient approximation accuracy of the cone crusher dynamics. During the process it is unstable due to the high sensitivity to disturbances on the output. The Hammerstein-Wiener model will be used to the design nonlinear model predictive control application.