Real-Time Acceleration Harmonics Estimation for an Electro-Hydraulic Servo Shaking Table Using Kalman Filter With a Linear Model

Yao, Jianjun; Di, Duotao; Jiang, Guilin; Gao, Shuang; Yan, Han

doi:10.1109/tcst.2013.2256136

Cited by 39 publications

(19 citation statements)

References 13 publications

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“…at is to say, the second harmonic is located in 10 Hz, the third harmonic in 10 Hz, and so on. As a critical indicator, total harmonic distortion (THD) is used to measure the waveform reproduction performance of sinusoidal signals [30]. e THD analysis result is shown in Table 1.…”

Section: E Sinusoidal Shakingmentioning

confidence: 99%

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table Based on Multi-Innovation Stochastic Gradient Algorithm

Wang

Yao

et al. 2020

Mathematical Problems in Engineering

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As one of the critical test equipment, hydraulic servo shaking table is widely used in shaking environment simulation of structural components and systems. However, inherent nonlinear factors of a hydraulic servo shaking table can cause amplitude attenuation and phase lag when corresponding to a sinusoidal acceleration signal, which leads to serious harmonic distortion. In order to improve waveform reproduction performance of sinusoidal signals, the amplitude and phase of harmonic should be estimated accurately. In this paper, the multi-innovation stochastic gradient (MISG) algorithm is presented for dynamically estimating the harmonic information. Simulation and experiment results demonstrate that the proposed algorithm has high estimation precision and good convergence performance.

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Section: E Sinusoidal Shakingmentioning

confidence: 99%

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table Based on Multi-Innovation Stochastic Gradient Algorithm

Wang

Yao

et al. 2020

Mathematical Problems in Engineering

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“…These make it suited to be applied in aerospace, automotive, machine, metallurgy and many other fields of heavy-industries [1]. Yet the electro-hydraulic servo shaking table also has plenty of disadvantages, namely, the dead zone of the servo valve, backlash connections, friction between the piston and the hydraulic cylinder, and the hydraulic oil pipeline geometry [2,3]. The dead zone is time-varying and asymmetrical, which is caused by the overlap of the slide valve and has a bad influence on static deviation of the system [4].…”

Section: Introductionmentioning

confidence: 99%

Acceleration Harmonics Identification for an Electro-Hydraulic Servo Shaking Table Based on a Nonlinear Adaptive Algorithm

et al. 2018

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Since the electro-hydraulic servo shaking table came into existence, many nonlinear elements, such as, dead zone, friction and backlash, as well as its acceleration response has higher harmonics which result in acceleration harmonic distortion, when the electro-hydraulic system is excited by sinusoidal signal. For suppressing the harmonic distortion and precisely identify harmonics, a combination of the adaptive linear neural network and least mean M-estimate (ADALINE-LMM), is proposed to identify the amplitude and phase of each harmonic component. Specifically, the Hampel’s three-part M-estimator is applied to provide thresholds for detecting and suppressing the impulse noise. Harmonic generators are used by this harmonic identification scheme to create input vectors and the value of the identified acceleration signal is subtracted from the true value of the system acceleration response to construct the criterion function. The weight vector of the ADALINE is updated iteratively by the LMM algorithm, and the amplitude and phase of each harmonic, even the results of harmonic components, can be computed directly online. The simulation and experiment are performed to validate the performance of the proposed algorithm. According to the experiment result, the above method of harmonic identification possesses great real-time performance and it has not only good convergence performance but also high identification precision.

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“…Yet the electro-hydraulic servo shaking table also has plenty of disadvantages, namely, blacklash connections, friction between the piston, the hydraulic cylinder, and the hydraulic oil pipeline geometry [2][3]. As a result of these nonlinearities existing in the servo system, the acceleration response of system generates high-order harmonics when the shaking table is excited by sinusoidal signal, which not only seriously leads to distortion of the acceleration response signal, but also lowers the dynamic tracing performance.…”

Section: Introductionmentioning

confidence: 99%

Acceleration Harmonics Identification for an Electro-Hydraulic Servo Shaking Table based on a Nonlinear Adaptive Algorithm

Yao¹,

Xiao²,

Wan³

et al. 2018

Preprint

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Since the electro-hydraulic servo shaking table exists many nonlinear elements, such as, dead zone, friction and blacklash, its acceleration response has higher harmonics which result in acceleration harmonic distortion, when the electro-hydraulic system is excited by sinusoidal signal. For suppressing the harmonic distortion and precisely identify harmonics, a combination of the adaptive linear neural network and least mean M-estimate (ADALINE-LMM), is proposed to identify the amplitude and phase of each harmonic component. Namely, the Hampel's three-part M-estimator is applied to provide thresholds for detecting and suppressing the error signal. Harmonic generators are used by this harmonic identification scheme to create input vectors and the value of the identified acceleration signal is subtracted from the true value of the system acceleration response to construct the criterion function. The weight vector of the ADALINE is updated iteratively by the LMM algorithm, and the amplitude and phase of each harmonic, even the results of harmonic components, can be computed directly online. The simulation and experiment are performed to validate the performance of the proposed algorithm. According to the experiment result, the above method of harmonic identification possesses great real-time performance and it has not only good convergence performance but also high identification precision.

show abstract

Real-Time Acceleration Harmonics Estimation for an Electro-Hydraulic Servo Shaking Table Using Kalman Filter With a Linear Model

Cited by 39 publications

References 13 publications

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table Based on Multi-Innovation Stochastic Gradient Algorithm

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table Based on Multi-Innovation Stochastic Gradient Algorithm

Acceleration Harmonics Identification for an Electro-Hydraulic Servo Shaking Table Based on a Nonlinear Adaptive Algorithm

Acceleration Harmonics Identification for an Electro-Hydraulic Servo Shaking Table based on a Nonlinear Adaptive Algorithm

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