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

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Xiancheng Wang ◽  
Wei Li ◽  
Jianjun Yao ◽  
Zhenshuai Wan
Keyword(s):  
Harmonic Distortion ◽  
Stochastic Gradient ◽  
Shaking Table ◽  
Gradient Algorithm ◽  
Phase Lag ◽  
Good Convergence ◽  
Acceleration Signal ◽  
Stochastic Gradient Algorithm ◽  
Hydraulic Servo ◽  
Convergence Performance

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.

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

2018 ◽  
Author(s):  
Jianjun Yao ◽  
Chenguang Xiao ◽  
Zhenshuai Wan ◽  
Shiqi Zhang ◽  
Xiaodong Zhang
Keyword(s):  
Dead Zone ◽  
Harmonic Distortion ◽  
Harmonic Component ◽  
Weight Vector ◽  
Shaking Table ◽  
Acceleration Response ◽  
Good Convergence ◽  
Acceleration Signal ◽  
True Value ◽  
Hydraulic Servo

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.

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

2018 ◽  
Vol 8 (8) ◽  
pp. 1332 ◽  
Author(s):  
Jianjun Yao ◽  
Chenguang Xiao ◽  
Zhenshuai Wan ◽  
Shiqi Zhang ◽  
Xiaodong Zhang
Keyword(s):  
Dead Zone ◽  
Harmonic Distortion ◽  
Harmonic Component ◽  
Weight Vector ◽  
Shaking Table ◽  
Acceleration Response ◽  
Good Convergence ◽  
Acceleration Signal ◽  
True Value ◽  
Hydraulic Servo

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.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

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