Structure and parameter identification of process models with hard non-linearities for industrial drive trains by means of degenerate genetic programming

verfasst von

Mathias Tantau, Lars Perner, Mark Wielitzka, Tobias Ortmaier

Abstract

The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Externe Organisation(en)

Lenze SE

Typ

Aufsatz in Konferenzband

Seiten

368-376

Anzahl der Seiten

9

Publikationsdatum

2019

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Information systems, Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.5220/0007949003680376 (Zugang: Eingeschränkt)
https://doi.org/10.15488/10398 (Zugang: Offen)