THE MODELLING OF A HYSTERESIS GRAPH OF PIEZOELECTRIC ELEMENTS USING DEEP LEARNING BIDIRECTIONAL LSTM

Abstract

The phenomenon of hysteresis is an integral part of dynamic systems in many fields of science such as physics, chemistry, biology and many more. It describes an inherent dependence of a system state based on the history of varying number of its previous states. Hysteresis can manifest as a dynamic lag between an input signal and an output system behaviour, which depends on the degree of that system dy-namics. Modelling systems containing hysteresis is a challenging mathematical task given their highly non-linear behaviour. This paper discusses and develop a deep learning model using bidirectional LSTM (long short-term memory) for predicting voltages necessary to stimulate a piezoelectric element to produce displacements in order to cancel or minimize vibrations. The predicted voltages rely on given displacements and time domain of the initial noise input. This noise input can then be amplified to match the resonance frequency of another piezoelectric element to generate the maximum voltage capable by this later piezoelectric element. This sinusoidal voltage then travels to a piezoelectric actuator to generate displacement that can cancel the initial noise. The model resulted a coefficient of determination score of 0.99983, a loss score of 0.0092 and MSE (mean squared error) of 8.5568e-05. Created model has proven that machine learning is a viable method for hysteresis modelling and can be further improved with increased input data availability and further investigation into different deep learning algorithms.