Papers by Keyword: System Identification

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Authors: Shuang Xi Zhang, Wen Gai, Wei Hua Chu, Nian Liu
Abstract: In wind tunnel experiments, in order to adjust the attack angle continuously, the support mechanism movement should be steady and smooth. However, the electro-hydraulic servo system is a typical nonlinear, time-varying and uncertainty system, and the wind tunnel environment is very complicated. To address these problems, an on-line identification and generalized predictive control (GPC) strategy is proposed in this study. Firstly, the Labview and AMESim are integrated to build an electro-hydraulic system simulation model. Secondly, the controlled auto-regressive moving average (CARIMA) model of the electro-hydraulic system is developed. Thirdly, the influence on the system performance owing to the control parameters, model parameters, and external disturbance are widely discussed and deeply analyzed. At last, a test platform is constructed with the National Instruments (NI) embedded real time technology. The proposed control strategy is tested and verified on this test platform. The experimental results show that the angular velocity control precision reaches 0.01°/s. It implies that this control strategy has a good performance for nonlinear velocity control. Thus it satisfies the requirement of the continuously adjusting attack angle in wind tunnel experiments.
Authors: Teresa Główka, Jarosław Figwer
Abstract: The aim of this paper is to present a method of nonparametric and parametric secondary path model identification for adaptive active noise control systems with low-power non-Gaussian excitations of the form of a higher-order discrete-time multisine random process and data processing based on cross-higher-order spectra. Properties of the discussed method are illustrated by simulation experiments devoted to secondary path identification for feedforward and feedback active noise control systems. Its robustness to nonlinear distortions implied by data acquisition system and adaptation procedure is proved.
Authors: Jian Wen Zhao, Sheng Rong Fan
Abstract: Correct identification of system parameters contributes to construct more precise model of the system and therefore improves the performance of controller. The current identification algorithms have some disadvantages, such as the complication of algorithms and the high requirement on hardware. In this paper, we propose a new system identification method which only utilizes the data from serve motor encoder. Based on the differential equations of motion and the theory of undetermined coefficients, mechanical parameters of the system can be solved using the least square method with the equations constructed based on kinematic parameters obtained by experimental tests. The identified mechanical parameters are then used to construct the system transfer function and a SIMULINK model. The method is validated by comparing the results between simulation and experiment. This method has advantages of low requirement on hardware and simple algorithm. It is proper to be applied in mechanical parameter identification of servo system.
Authors: Tzu Kang Lin, Ming Chih Huang, Jer Fu Wang
Abstract: A bridge health monitoring system based on neural network technology is proposed in this paper. Two major ground excitations recorded in Taiwan were used to establish the NARX-based system. Analytical results from different methods including transfer function, ARX-based model, and the proposed neural network-based system were used to evaluate the efficiency in health monitoring. The result shows that the proposed system can be used successfully with superior advantages after major earthquakes for bridge health monitoring.
Authors: Behnam Adhami, Hamed Niroumand, Karen Khanlari
Abstract: In general terms, the aim of "System Identification" is to determine the dynamic characteristics of mechanical systems. These characteristics include both frequency characteristics (frequencies, mode shapes, and damping ratios) and the system's characteristic matrices (the matrices of mass, viscous damping, stiffness, Coulomb damping or coefficients of friction, and the Duffing stiffness). In such fields as "Damage Detection" in structures, identification of the system's characteristic matrices is of the same importance as the identification of the frequency characteristics, or even more so, by identifying these matrices, the intended goals in Damage Detection can be achieved. In line with such identification, a new algorithm for the system identification of shear structures is presented in the paper. Taking into account the fundamental and significant effect of noise attenuation in boosting the levels of precision and the correctness of system identification, this method helps to achieve noise attenuation by trimming noisy records in the frequency domain, in parallel with the identification of the structural system. The efficiency and precision of the method have been examined through the application of a "closed loop solution" to a five storey model of shear structure.
Authors: Chung Neng Huang, Chen Min Cheng
Abstract: This study proposes a new modeling method for unknown systems. Through this method, the transfer functions can be identified. First, the input-output data pairs of the unidentified system should be collected. Then, the transfer function’s coefficients can be identified based on the errors via the derivative-free search methods such as GA etc. Here, a second-order transfer function is used in this study. For a second-order transfer function is difficult to approach each system, a plurality of transfer functions may be used depending on the precise requirement. Finally, following the previous steps, the other transfer function can be found in succession. In order to confirm the effectiveness of this proposed method, an electromagnetic flywheel (EF) system is used in this study. Such kinds of systems are always with many uncertainties as nonlinear electromechanical coupling and electromagnetic saturation, etc. They are difficult to modeling via traditional mathematic ways. In this study, the data pairs of EF system is collected by experiments. By assessing the results of the proposal and experimental data shows that this method is feasible to any unknown systems. system, achieve more saving energy and high efficiency control purposes.
Authors: Kuo Hsiung Tseng, Yong Fong Shiao, Yu Ting Yeh
Abstract: This study discussed the application of microwave-based heating for the pretreatment of biomass material, and selected Pennisetum purpureum for pretreatment. The Taguchi method was used to plan optimization experiments for pretreatment parameter levels, and measured the dynamic responses. With lower frequency of experiments, this study analyzed and determined a parameter combination in which Pennisetum purpureum can be rapidly heated to 190°C. The experimental results indicate an eight-order ARX model (Auto-Regressive eXogeneous) was representative of actual system performance, and the fit was 99.13%.
Authors: Asnizah Sahekhaini, Pauziah Muhamad, Masayuki Kohiyama, Aminuddin Abu, Lee Kee Quen, Hanida Abdullah
Abstract: This paper presents a wavelet-based method of identification modal parameter and damage detection in a free vibration response. An algorithm for modal parameter identification and damage detection is purposed and complex Morlet wavelet is chosen as an analysis wavelet function. This paper only focuses on identification of natural frequencies of the structural system. The method utilizes both undamaged and damage experiment data of free vibration response of the truss structure system. Wavelet scalogram is utilizes for damage detection. The change of energy components for undamaged and damage structure is investigated from the plot of wavelet scalogram which corresponded to the detection of damage.
Authors: Ying Lei, Yi Ke Mao
Abstract: In this paper, a time domain structural damage detection approach based on Kalman filter and least square estimation is proposed for structures under limited input and output measurements. First of all, the dynamic parameters of the structure, such as stiffness and damping coefficients are identified by sequentially utilizing the extended Kalman estimator and the least squares estimation. Secondly, after the structural dynamic parameters are gained, the normal Kalman filter and least square estimation is used to identify the state vector and unknown input of the structure, and the changes of the structure such as reducing of the stiffness are regarded as ‘additional unknown input’. Then the changed parameters can be calculated by analyses the connection between ‘additional unknown input’ and changing parameters, Local structural damage in the structure can also be detected by tracking the changes in the identified values of structural dynamic parameters in time domain at element level, e.g., the degrading of stiffness parameters. Numerical example of detecting structural local damages in a four story share building illustrates the efficiency of the proposed approach.
Authors: Ying Lei, Yong Qiang Jiang
Abstract: Detection of structural damages is critical to ensure the reliability and safety of structures. So far, some progresses in structural identification have been made. The extended Kalman filter (EKF) has been one of the classic time-domain approaches for the identification of structural parameters. However, since the extended state vector contains both the state vector and the structural parameters, EKF approach can identify limited numbers of nonlinear structural parameters due to computational convergence difficulty. To overcome such problem, a two-stage Kalman estimation approach, which is not available in the previous literature, is proposed for the identification of structural parameters. In the first stage, state vector of structures is considered as an implicit function of the structural parameters, and the parametric vector is estimated directly based on the Kalman estimator. In the second stage, state vector of the structure is updated by applying the Kalman estimator with the structural parameters being estimated in the first stage. Therefore, analytical recursive solutions for the structural parameters and state vector are respectively derived and presented, by using the Kalman estimator method in a two-stage approach. The proposed approach is straightforward. Moreover, it can identify more numbers of nonlinear structural parameters with less time of iteration calculation compared with the conventional EKF. A numerical example of identifying the parameters of an 8-storey shear-frame structure is conducted. Simulation results show that the proposed approach is effective and accurate.
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