On the Issue of Simulating Dynamics of a Pneumo-Mechanical Device

Authors: Zelenov M.S., Atamasov N.V., Chernyshev A.V. Published: 07.12.2018
Published in issue: #6(123)/2018  

DOI: 10.18698/0236-3941-2018-6-20-33

Category: Mechanical Engineering and Machine Science | Chapter: Machine Science  
Keywords: pneumatic systems, dynamics prediction, neural network, neural emulator, training dataset, mathematical simulation

The paper considers the stages of developing an artificial neural network (a neural emulator) that predicts variation in parameters of state for a pneumatic spring operating under variable external loads. We present a mathematical model for predicting work cycles that is used to develop neural network algorithms for controlling pneumatic drives. In order to investigate the efficiency of this approach, we developed a program of artificial neural network training based on training datasets derived from numerical experiments using the work cycle model built on the basis of first-order differential equations. We describe the training sample structure and the principles behind generating training datasets. We used the Levenberg --- Marquardt algorithm to train our network and tested the trained neural emulator on the results of series of experiments with random initial parameters taken from a specific bounded domain. We estimated prediction efficiency by an integral criterion: the average coefficient of determination, computed separately for each prediction distance and every model output parameter, such as piston position, piston velocity and pressure difference between the pneumatic spring chambers. Comparative test experiment results show that the data


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