Identification of Acceleration Harmonics for a Hydraulic Shaking Table by Using Hopfield Neural Network

The paper aims to develop a harmonic identification scheme for a hydraulic shaking table’s sinusoidal acceleration response. Nonlinearities are inherent in a hydraulic shaking table. Some of them are dead zone of servo valve, backlash and friction between joints, and friction in actuator. Nonlinearities cause harmonic distortion of the system shaking response when it correspondsto a sinusoidal excitation. This lowers the system control performance. An efficient, time-domain acceleration harmonic identification is developed by using Hopfield neural network. Due to the introduction of energy function used to optimize the computation for the identification harmonic method, the fully connected, single layer feedback neural network does not require training in advance and is able to identifyharmonic amplitudes and phase angles. Each harmonic,as well asthe fundamental response,can be directly obtained.Simulations and experiments show very promising results that the proposed scheme is really applicable to identify harmonicswith high precision and good convergence. Comparisons between the presented method and other method are carried out to further demonstrate its efficiency.