Grey wolf optimization for intelligent parametric modeling of gradient flexible plate structure.

Abstract

This paper presents the dynamic modeling of the gradient flexible plate system using System Identification method based on autoregressive with exogenous input model structure and estimated by Grey Wolf Optimization. The experimental rig of the gradient flexible plate was integrated with the data acquisition and instrumentation to obtain input-output vibration data. The performances of developed models were validated through one step ahead prediction, mean squared error, and correlation tests. The model was verified using the pole-zero diagram to confirm its stability for the controller development. Results indicated that the optimum model to represent the dynamic system of gradient flexible plate was achieved by model order 4 with the mean squared error of 8.0496×10-6. The correlation results proved that the model was unbiased, and falls within the 95% confidence level. Likewise, the model was found to be stable as all the poles of transfer function were within the unit circle. Therefore, the identified model can be confidently used for the controller development to suppress undesirable vibration in the gradient flexible plate structure.