Fixed-Time Stabilizing Control for Nonlinear Systems With Uncontrollable Linearization and Unmodeled Dynamics

This paper addresses the problem of fixed-time stabilization for a class of nonlinear systems.Unlike existing results in the literature, the studied system accounts for uncontrollable unstable linearization and unmodeled dynamics, which may represent a broader class of nonlinear systems. Thanks to the changing supply rates for fixed-time input-to-state stability (FxT-ISS) system and the adding-one-power-integrator (AOPI) techniques, we propose a partial-state feedback control scheme that ensures system states converge to zero in fixed time. The settling time under this fixed-time control scheme depends solely on the control design parameters, independent of the initial conditions. Finally, numerical simulations validate the effectiveness of the proposed method.

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Publication Details

Published
2024-11-01
DOI
https://doi.org/10.1109/cac63892.2024.10864734
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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Controls
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article

Fixed-Time Stabilizing Control for Nonlinear Systems With Uncontrollable Linearization and Unmodeled Dynamics

Xinyu Wang, Yan Zhao, Jiangbo Yu
Iterative Learning Control Systems
article

Fixed-Time Stabilizing Control for Nonlinear Systems With Uncontrollable Linearization and Unmodeled Dynamics

Xinyu Wang, Yan Zhao, Jiangbo Yu
article en

Abstract

This paper addresses the problem of fixed-time stabilization for a class of nonlinear systems.Unlike existing results in the literature, the studied system accounts for uncontrollable unstable linearization and unmodeled dynamics, which may represent a broader class of nonlinear systems. Thanks to the changing supply rates for fixed-time input-to-state stability (FxT-ISS) system and the adding-one-power-integrator (AOPI) techniques, we propose a partial-state feedback control scheme that ensures system states converge to zero in fixed time. The settling time under this fixed-time control scheme depends solely on the control design parameters, independent of the initial conditions. Finally, numerical simulations validate the effectiveness of the proposed method.

Shandong Jianzhu University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 67%
Iterative Learning Control Systems
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