Modeling and control of one link flexible arm using mathematical expression extraction from neural network

Abstract

In recent decades, a number of researches has been focused on development and control of soft robots. There is one approach that uses a neural network to learn a forward model of a robot and control the robot by extracting equivalent equations from the neural network. However, if the training data obtained from the robot only contains partial dynamic characteristics, the linear state equation extracted from the neural network can be barely controlled. In this research, we propose a method to use singular value decomposition in order to reflect deviation of training data. We confirmed that our proposed method can control a flexible arm by simulation.