Intelligence Swarm Model Optimization of Flexible Plate Structure System

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This paper presents the performance of modeling the flexible plate structure with free-free-clamped-clamped (FFCC) edges boundary condition using conventional Recursive Least Squares (RLS) and evolutionary algorithm of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The auto-regressive with exogenous (ARX) structure was used in this study to obtain the dynamic model of a flexible plate structure. Data acquisition and instrumentation system were designed and integrated with the experimental rig and several experimental procedures were conducted to acquire the input and output of the flexible plate. The input and output data collected from the experimental study were utilized to develop the model of the system. The 4000 data sets collected in the experiment were divided into two parts for training and testing. The first 3000 data sets were used to train the model developed while the last 1000 data sets were used to test the performance of thus developed model. All developed model using RLS, GA and PSO were validated using one step-ahead prediction (OSA), mean squared error (MSE) and correlation tests. Amongst all, it was found that PSO algorithm has performed better in term of lowest mean squared error achieved (0.00032719) as compared to conventional algorithm (RLS) and evolutionary algorithm (GA). However, by comparing in term of estimating the first mode of vibration which the dominant mode of structure, GA has performed better by presented the lowest percentage error (3.63 %). Besides that, it was found that, all 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 the flexible plate structure.
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Evolutionary Optimization; Genetic Algorithm; Flexible plate system; System Identification; Particle Swarm Optimization

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