Cone Crusher Model Identification Using Block-Oriented Systems with Orthonormal Basis Functions
For engineers designing control systems for cone crushers, this work provides a more accurate model for nonlinear control, though it is an incremental application of existing methods.
This paper identifies a cone crusher model using block-oriented systems with orthonormal basis functions, achieving an average mean square error of 11% with the Hammerstein-Wiener structure, which outperforms other models and will be used for nonlinear model predictive control.
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.