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Flexible beam structure is usually applied in various fields of engineering and industrial. There are few points of interest using flexible structure and one of the advantages is that its lighter compared to a rigid structure. Besides that, flexible beam also can save cost, reduce energy consumption, and improve operation safety. However, flexible beam structures are too sensitive and susceptible to with unwanted vibration that would cause damage or degradation to the structure system. Hence, to overcome the problem, appropriate modelling and controller for such systems should be developed. Currently, there are plenty of methods that have been developed by researchers to suppress undesired vibration. Based on previous studies, most researchers nowadays use system identification (SI) as a modelling technique to develop a dynamic model of flexible structure via swarm intelligence algorithm (SIA). Therefore, two type of algorithms was used in this work for modelling development of flexible beam structure, which are ant colony optimization (ACO) and cuckoo search algorithm (CSA). Based on the comparative results, CSA achieved the lowest mean square error (MSE) value of 6.1547×10 -9 meanwhile ACO recorded a MSE of 1.0728×10 -8 . Moreover, CSA was deduced to be the best model for flexible beam structure because it achieved 95 % confidence level in correlation test and has excellent stability in pole-zero diagram system. Thus, CSA is a suitable algorithm to represent the real behavior of flexible beam structure in a system.

<p>A flexible beam is recognized as a lightweight structure that is prone to excessive vibration, resulting in poor performance. Thus, controlling unwanted vibration is necessary to maintain the system’s performance. Therefore, this study presents a technique to suppress undesired vibration in a flexible beam structure by introducing active vibration control (AVC). However, to develop an effective controller, an appropriate flexible beam model must first be obtained. In recent times, one of the best methods employed to model a flexible beam structure is system identification via a swarm intelligence algorithm. In this study, an intelligent algorithm acknowledged as cuckoo search (CS) was acquainted. The capability of the proposed algorithm was verified using three robustness techniques which were correlation test, pole-zero diagrams and mean square error (MSE). The simulation result showed that the CS algorithm achieved superior performance by achieving the lowest MSE of 6.1547x10^-9, a correlation test between a 95% confidence level and high stability. Next, a proportional-integral-derivative (PID) controller tuned by the Ziegler-Nichols method was developed using the transfer function accomplished from the CS model. Two types of interference, namely single and multiple sine waves were introduced to validate the effectiveness of the controller. The controller successfully achieved a 30.2 dB of attenuation level for both disturbances.</p>

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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.