Data Set Description: Identifying the Physics Behind an Electric Motor – Data-Driven Learning of the Electrical Behavior (Part II)

03/13/2020
by   Sören Hanke, et al.
0

A data set was recorded to evaluate different methods for extracting mathematical models for a three-phase permanent magnet synchronous motor (PMSM) and a two-level IGBT inverter from measurement data. It consists of approximately 40 million multidimensional samples from a defined operating range of the drive. This document describes how to use the published data set <cit.> and how to extract models using introductory examples. The examples are based on known ordinary differential equations, the least squares method or on (deep) machine learning methods. The extracted models are used for the prediction of system states in a model predictive control (MPC) environment of the drive. In case of model deviations, the performance utilizing MPC remains below its potential. This is the case for state-of-the-art white-box models that are based only on nominal drive parameters and are valid in only limited operation regions. Moreover, many parasitic effects (e.g. from the feeding inverter) are normally not covered in white-box models. In order to achieve a high control performance, it is necessary to use models that cover the motor behavior in all operating points sufficiently well.

READ FULL TEXT
research
03/16/2020

Data Set Description: Identifying the Physics Behind an Electric Motor – Data-Driven Learning of the Electrical Behavior (Part I)

Two of the most important aspects of electric vehicles are their efficie...
research
09/10/2021

KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots

In this work, we consider the problem of deriving and incorporating accu...
research
10/21/2019

Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control

Electric motors are used in many applications and their efficiency is st...
research
09/19/2022

RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Network

Model Predictive Control (MPC) is a state-of-the-art (SOTA) control tech...
research
02/16/2020

Nonlinear MPC with Motor Failure Identification and Recovery for Safe and Aggressive Multicopter Flight

Safe and precise reference tracking is a crucial characteristic of MAVs ...
research
10/29/2018

Identification of physical processes via combined data-driven and data-assimilation methods

With the advent of modern data collection and storage technologies, data...

Please sign up or login with your details

Forgot password? Click here to reset