Adaptive Gain Super-Twisting-Algorithm: Design and Discretization

Lukas Eisenzopf, Stefan Koch, Lars Watermann, Markus Reichhartinger, Johann Reger, Martin Horn

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


In this paper, an eigenvalue-based discretization scheme is applied to a novel adaptive super-twisting-algorithm. Following the proposed procedure the discretization chattering effect is avoided entirely. An attractive property of the adaptation law is the insensitivity of the closed-loop system to overly large gains which in existing laws potentially leads to instability. Using Lyapunov's direct method the stability of the feedback loop is shown. Numerical examples underline the beneficial properties of the proposed methodology.
Original languageEnglish
Title of host publication60th IEEE Conference on Decision and Control, CDC 2021
Number of pages6
ISBN (Electronic)9781665436595
Publication statusPublished - 2021
Event60th IEEE Conference on Decision and Control: CDC 2021 - Virtuell, United States
Duration: 13 Dec 202115 Dec 2021

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference60th IEEE Conference on Decision and Control
Abbreviated titleCDC 2021
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Control and Optimization
  • Control and Systems Engineering
  • Modelling and Simulation


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