Abstract
In the metallurgical industry, the hydraulic automatic gauge control (HAGC) system is a kernel system used to assure the thickness precision of a rolling piece. In addition, the HAGC system is a complex nonlinear system with multiple degrees of freedom (DOF). It is difficult to identify the key parameters and obtain an accurate prediction model that can reflect the output performance of the HAGC system. On the basis of theoretical analysis, a vertical vibration model of the HAGC system with two DOF was established. Moreover, based on the swarm intelligence in nature, a novel method for model parameter identification of the HAGC system was explored, and a parameter identification method was proposed based on the chaotic wolf pack optimization algorithm. Furthermore, the proposed method was verified by experiments. The results indicate that the proposed method presents laudable identification ability. The theoretically predicted response of the identification model is consistent with the measured response of the actual system. The error of the response output waveform is small. The proposed method can be used to identify key structural parameters of the HAGC system.
Highlights
The method adopted to identify the key parameters of the hydraulic automatic gauge control (HAGC) system as well as the acquisition of a more accurate prediction model that can reflect the performance of the HAGC system is still worthy of further study
In order to further confirm the effectiveness of parameter identification based on the chaotic wolf pack optimization (CWPO) algorithm, a parameter identification experiment is performed
Through theoretical research and experimental verification, the results indicate that the parameter identification method based on the CWPO algorithm possesses strong identification ability
Summary
The hydraulic automatic gauge control (HAGC) system is a kernel system used to guarantee the thickness of the rolling piece with high precision. The identification of the key parameters in the HAGC system is a frontier research problem in the academic field. The method adopted to identify the key parameters of the HAGC system as well as the acquisition of a more accurate prediction model that can reflect the performance of the HAGC system is still worthy of further study. The wolf pack search algorithm has been widely applied in many aspects, through thorough study it has been found that the LWPS algorithm still has the following disadvantages: First, when competing for the leader wolf, the step-size of search is constant, which is unable to be adaptively adjusted. This affects the search precision of the local optimum solution.
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