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

Abstract

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 each control system into two representation vectors: a first vector depicting the motor characteristics and its operating mode and a second vector describing the controller effect. We test our method on a scenario where we simulate the behavior of two different controlled DC motors. We map the behavior of a controller of a lab motor to a field motor. The experiments show that DR-TiST can recognize motor and controller characteristics and predict the right behavior.