TY - GEN
T1 - Adaptive Gain Super-Twisting-Algorithm: Design and Discretization
AU - Eisenzopf, Lukas
AU - Koch, Stefan
AU - Watermann, Lars
AU - Reichhartinger, Markus
AU - Reger, Johann
AU - Horn, Martin
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85126062306&partnerID=8YFLogxK
U2 - 10.1109/CDC45484.2021.9683304
DO - 10.1109/CDC45484.2021.9683304
M3 - Conference paper
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 6415
EP - 6420
BT - 60th IEEE Conference on Decision and Control, CDC 2021
T2 - 60th IEEE Conference on Decision and Control
Y2 - 13 December 2021 through 15 December 2021
ER -