Genetic modeling of a rectangular flexible plate system with free-free-clamped-clamped (FFCC) edges

This paper presents the performance of system identification of a rectangular flexible plate structure using Least Squares, Recursive Least Squares and Genetic Algorithms techniques. The input - output data are collected through an experimental study using a vibrational flexible plate experimental rig complete with data acquisition and instrumentation system. The experimental rig of two clamped and two free edges (FFCC) flexible plate were fabricated and used throughout this research. All the models developed using Least Squares, Recursive Least Squares and Genetic Algorithms were validated using one step-ahead prediction (OSA), mean squared error (MSE) and correlation tests. It was found that the estimated models using all methods proposed are comparable, acceptable and possible to be used as a platform of controller development and verification to suppress the vibration of flexible plate structure in the future work. Amongst all, it was found that the Genetic Algorithm has performed the best as compared to other methods in estimating the first mode of vibration which is the dominant mode of the structure with 3.63% error. However, for overall estimation, the Least Squares algorithm has achieved the lowest mean squares error (0.00019725) as compared to the Recursive Least Squares and Genetic Algorithms performance.