Modeling flexible plate structure system with free-free-clamped-clamped (FFCC) edges using Particle Swarm Optimization
This paper presents the performance of system identification for rectangular flexible plate using conventional parametric modeling approach by Recursive Least Squares (RLS) and intelligent parametric modeling approach by Particle swarm Optimization (PSO). The experimental rig of rectangular flexible plate with free-free-clamped-clamped edges boundary condition designed and fabricated in this research. The experimental study was conducted to collect the input-output data using a vibration flexible plate complete with data acquisition and instrumentation system. The input-output data will be used during developed the system identification. The whole developed model using RLS and PSO were validated using mean squares error (MSE), one step ahead prediction (OSA) and correlation test. The estimated of developed models was found are comparable, acceptable and possible to be used as a platform of controller development later on. As a comparison between developed system, it was found that the Particle Swarm Optimization algorithm has perform better in term of the lowest mean squares error which is 0.00032719.
System identification of flexible plate structure
This research presented an investigation into the performance of system identification using parametric and nonparametric techniques for the identification of a two-dimensional flexible plate structure. The input and output data of the flexible system were acquired through the experimental work using National Instrumentation data acquisition system and flexible plate test rig. A sinusoidal force was applied to excite the flexible plate and the dynamic response of the system was investigated. The parametric models of the system were developed through Recursive Least Square (RLS) and Genetic Algorithms (GA) methods; whilst the nonparametric models of the system were developed using Multi-layer Perceptron Neural Networks (MLP-NN), Adaptive Elman Neural Networks (ENN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The validity of the models was investigated based on statistical measures, mean square error and one step ahead output prediction. A comparative performance of all the approaches developed in this research was presented and discussed. It has been demonstrated that the best mean squared error for RLS was 0.0095 and for GA algorithm was 0.000562. This indicates the superiority of GA as compared to RLS for the parametric modelling approaches. For the nonparametric modelling of the system, the best mean squared error for MLP-NN, ENN and ANFIS were 0.000163, 0.001700 and 0.0003978, respectively. The results demonstrated that MLP-NN shows superiority as compared to ENN and ANFIS. The investigation also revealed that, comparing to all modelling techniques, MLP-NN performed the best in terms of convergence time to an optimum solution.
Geometrically nonlinear vibration analysis of thin, rectangular plates using the hierarchical finite element method—I: The fundamental mode of isotropic plates
Abstract After successful applications of the hierarchical finite element method (HFEM) to geometrically nonlinear static analysis of laminated plates, and linear free and forced vibration analysis of laminated plates, the geometrically nonlinear free vibration of isotropic rectangular plates with fully clamped edges is analysed using the HFEM in this paper. The von Karman type of geometrically nonlinear strain-displacement relationships, and harmonic balance method were used in deriving the equation of motion. The influences of large vibration amplitude on the mode frequency, mode shape and mode stresses of the fundamental mode are discussed. A modified form of Berger's hypothesis was employed to study the in-plane membrane forces averaging effect on the geometrically nonlinear behaviour. A cancellation effect was discovered. The nonlinear frequencies predicted by the present method were compared with both numerical and experimental results from the published literature. An excellent agreement was found.