Implementation of swarm algorithm in modeling a flexible beam structure

Abstract

The application of System Identification techniques for modeling a flexible beam structure are presented in this paper. The flexible beam has been widely applied in various fields engineering and industrial. However, the flexible structure is easily influenced by unwanted vibration which may lead to fatigue, performance reduction and structure damage. Thus, the unwanted vibration must be controlled and reduced. In order to have a good controller performance for vibration suppression, an appropriate model of flexible beam is required. Hence, to obtain a model of the flexible beam structure, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are implemented in this study as System Identification techniques. The implementation of PSO and ABC requires experimental data input and output retrieved from data acquisition from a well-developed experimental test rig via MATLAB Simulink platform. Results obtained are displayed in graphical plots and numerical values. The predicted model is validated via mean square error (MSE) and correlation tests. To represent the dynamic model of the flexible beam structure, model with minimum MSE value and correlation test within 95 % confidence interval is selected as the best fit model. The result shows that PSO algorithm produces better performance compared to ABC algorithm with a 3rd order predicted model that has lowest MSE value and correlation tests within 95 % confidence interval for the beam system.