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System identification of a composite plate using hybrid response surface methodology and particle swarm optimization in time domain
Affiliation:1. National Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, PR China;2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, PR China;3. Department of Engineering, University of Glamorgan, Pontypridd CF37 1DL, UK;1. Instituto de Telecomunicações, DEEC, IST, UL, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal;2. Instituto de Telecomunicações, Universidade de Évora, Rua Romão Ramalho 59, 7000-671 Évora, Portugal;1. Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1001 Ljubljana, Slovenia;2. Metrel d.d., Ljubljanska cesta 77, SI-1354 Horjul, Slovenia
Abstract:Material properties of composites are identified using a novel hybrid RSM–PSO method in this paper. Different response surface methodology (RSM) methods and particle swarm optimization (PSO) methods are studied initially on a 4 degrees-of-freedom (4DOF) dynamic system on their performance in terms of speed and accuracy. The best combination is used as a hybrid RSM–PSO method to evaluate the performance on system identification of an orthotropic plate along with a 4DOF dynamic system and an isotropic plate. The novelty of the present paper is to identify the composite plate material properties using RSM methods based on time domain signals, which is not hitherto reported in the literature. Also, whereas previous papers have used full factorial design for system identification, here CCDI is used. The input factors (design variables) are the system parameters which are to be identified and the response (objective function) is error sum-of-square of acceleration response with respect to new test system. The performance of the proposed method is also evaluated with the addition of 5% Gaussian noise to simulate the experimental errors. The system parameters of the orthotropic plate were identified with 0% and 0.25% average prediction error with zero and 5% addition of noise respectively by the proposed hybrid RSM–PSO method. It is also showed a much better performance and robustness to noise addition when compared to the other RSM methods in the literature.
Keywords:Composites  System identification  Response surface methodology (RSM)  Particle swarm optimization (PSO)  Time domain
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