Research on PSD Replication for Multiaxial Hydraulic Vibration Test System Based on FXLMS Algorithms

Yunbin Cheng,Bin Li,Jun Wang,Heliang Li,Jiaxi Zhou

doi:10.1155/2021/6610817

Abstract

The multiaxial electrohydraulic vibration test system can not only simulate the multiexcitation on multidimensional vibration environment but also meet the vibration test requirements of high thrust, large displacement, low frequency, etc. In order to eliminate the effects of nonlinear factor and system noise and achieve a more accurate control result, a control algorithm based on the frequency-domain filtered-x least mean square adaptive algorithm (FXLMS) is proposed to achieve the power spectral density (PSD) replication. The main idea is to use the frequency-domain FXLMS algorithm to adjust the controller adaptively corresponding to the transfer function uncertainty and changes of the plant, which are typically caused by time-varying parameters in electrohydraulic actuators and system noise. The details and implantation steps of the proposed algorithm are analysed for the single-input single-output electrohydraulic vibration test system. The proposed algorithm and control strategy are then extended to the multiaxial electrohydraulic vibration test system. Eventually, some experimental targets for performing the PSD replication test on a two-exciter system are carried out, in which the results show that the proposed algorithm is valid and meets the test standards..

Highlights

Yang et al proposed an adaptive inverse control of random vibrations based on the Filtered-x Least Mean Square (FXLMS) algorithm, and the frequency-domain least mean squares (LMS) algorithm was adopted to refine the inverse characteristics of the frequency response functions (FRFs) [13]
Shen et al [14] proposed a control strategy combining the adaptive inverse control (AIC) and inverse frequency response function (IFRF) and applied it to the electrohydraulic shaking table (EHST). e performance of system frequency bandwidth and asymptotic reference tracking was significantly improved in their experiment compared with traditional PID and IFRF controls
We propose an improved AIC based on the FXLMS algorithm and test it on a multiaxial hydraulic vibration test system. e algorithm is tested on a two-exciter system, and the experimental results suggest that it can significantly reduce the nonlinearity and noise effect

Summary

Introduction

Dynamics testing is important for detecting the mechanical properties of structures and products [1]. e electrohydraulic shaking table (EHST) is one of the most important vibration test system and widely used in the fields of architectural engineering [2], civil engineering [3], earthquake resistance testing [4, 5], mechanical fatigue [6], structural testing [1], and so on. ere are two categories of shaking tables targeting different control objects, namely, displacement-controlled and acceleration-controlled hydraulic shaking tables. e majority of the early EHST belongs to the former due to a simple control scheme, which has the shortcomings of a complex operation. e accelerationcontrolled hydraulic shaking tables are widely adopted [3]. Salehzadeh-Nobari et al developed the frequency-domain FXLMS from Ling Dynamic System (LDS) and applied it to the shock vibration test of the vibration table [11]. Karshenas et al proposed an impact test algorithm based on the LMS control algorithm, which incorporates the standard FXLMS control structure with the frequency-domain adaptive filter [12]. Yang et al proposed an adaptive inverse control of random vibrations based on the FXLMS algorithm, and the frequency-domain LMS algorithm was adopted to refine the inverse characteristics of the frequency response functions (FRFs) [13]. Shen et al [14] proposed a control strategy combining the AIC and inverse frequency response function (IFRF) and applied it to the EHST. We propose an improved AIC based on the FXLMS algorithm and test it on a multiaxial hydraulic vibration test system. e algorithm is tested on a two-exciter system, and the experimental results suggest that it can significantly reduce the nonlinearity and noise effect

Theory and Method

Results and Discussions

Summary