Translation of Time Series Data from Controlled DC Motors using Disentangled Representation Learning

In this paper, we consider the problem of translating time series of one controlled DC motor to imitate time series from another motor. Our main goal is to test different controllers and find the best performing controller for a motor operating in the field without knowing its mathematical model. By means of representation disentanglement, we present a new approach that splits the time series of e...