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Research On Key Technologies Of Seismic Simulation Vibration Table Control

Posted on:2020-10-22Degree:MasterType:Thesis
Country:ChinaCandidate:J KongFull Text:PDF
GTID:2370330572469963Subject:Control Engineering
Abstract/Summary:PDF Full Text Request
The seismic simulation vibration table is a key equipment mainly used in structural seismic tests.It can simulate the seismic environment and verify the seismic performance of the structure.It is of great significance to develop seismic simulation shaking tables that can meet the requirements of seismic simulation tests,and to promote the research of structural earthquake resistance and the development of earthquake prevention and disaster reduction in China.Among them,the research on seismic simulation shaking table control system is an important research topic.The seismic simulation test requires that the vibration table system has a certain working bandwidth and can achieve a certain seismic wave reproduction accuracy.In order to meet the above requirements,the seismic simulation shaking table control system generally includes a servo control system and a vibration control system.In this paper,the key technologies of seismic simulation vibration table control are deeply studied,including the following work:1.Three-variable control strategy for seismic simulation shaking table.For the servo control system,this paper studies the three-variable control strategy and studies the correction effect of the three-variable control on the system characteristics.In this paper,the function realization of the three-variable signal generator in the three-variable control is studied and analyzed.A three-variable reference signal generator based on time domain integration and a three-variable reference signal generator based on frequency domain integration are proposed to improve the integration precision and the effect of the three-variable control.2.System identification of the seismic simulation shaking table.Accurate system identification of the servo control system is the basis for the realization of the function of the vibration control system.In this paper,the non-parametric frequency response function estimation method is used to realize the identification of the acceleration closed-loop transfer function of the servo control system.A sinusoidal frequency-based acceleration excitation signal is specially designed to identify the vibration table system and ensure that the system identification will meet the test equipment parameters and avoid damage to the test equipment.3.Iterative learning control of the seismic simulation shaking table.Based on the system identification,the open-loop iterative learning control method is adopted,and the iterative learning control algorithm and control flow of the seismic simulation shaking table are proposed.Aiming at the hysteresis problem of the control system existing in the seismic simulation shaking table test,an iterative learning control algorithm based on time delay estimation is proposed.The convergence of the algorithm is demonstrated and the corresponding algorithm flow is proposed.4.Seismic simulation shaking table experiments.Through the construction of a single free seismic simulation shaking table experimental platform,the experimental control and functional verification of the seismic simulation shaking table control algorithms were carried out.The experimental results show that:(1)The improved three-variable signal generator proposed in this paper has higher control precision than the traditional three-variable signal generator;(2)After using the three-variable control strategy,the working bandwidth of the servo control system meets the requirements;(3)The iterative learning control algorithm based on time-delay estimation proposed in this paper can make the algorithm converge in the actual situation where the control system is lagging,and achieve high-precision seismic waveform reproduction through multiple iterative control experiments.
Keywords/Search Tags:Seismic simulation vibration table, Three-variable control, System classification, Iterative learning control
PDF Full Text Request
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