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

Abstract

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.

A comprehensive overview of the ADALINE method applied to rapid voltage sags detection in multi-motors drive systems

“…The output is a linear combination of the N inputs. The ADALINE was first used as an adaptive method to estimate the amplitude and phase of the fundamental and harmonics of a distorted signal [18], [19]. The simplicity of this method is due to its simple calculations (essentially multiply and accumulate "MAC" calculations), which facilitate its implementation.…”

Research on parameter identification of shaking table systems based on the RLS method

“…The shaking table is a very important device for simulating earthquakes in the laboratory and provides an accurate method for measuring structural responses under seismic conditions [1][2][3][4]. With the development of the shaking table, it has become an effective measure for studying the elastoplastic seismic response of structures [5].…”