Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 27 June 2018 / Approved: 28 June 2018 / Online: 28 June 2018 (10:55:10 CEST)

A peer-reviewed article of this Preprint also exists.

Yao, J.; Xiao, C.; Wan, Z.; Zhang, S.; Zhang, X. Acceleration Harmonics Identification for an Electro-Hydraulic Servo Shaking Table Based on a Nonlinear Adaptive Algorithm. Appl. Sci. 2018, 8, 1332. Yao, J.; Xiao, C.; Wan, Z.; Zhang, S.; Zhang, X. Acceleration Harmonics Identification for an Electro-Hydraulic Servo Shaking Table Based on a Nonlinear Adaptive Algorithm. Appl. Sci. 2018, 8, 1332.

Journal reference: Appl. Sci. 2018, 8, 1332
DOI: 10.3390/app8081332

Abstract

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.

Subject Areas

harmonic identification; adaptive linear neutral network; least mean M-estimate; electro-hydraulic servo shaking table; harmonic distortion

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