Issue |
ESAIM: COCV
Volume 28, 2022
|
|
---|---|---|
Article Number | 30 | |
Number of page(s) | 22 | |
DOI | https://doi.org/10.1051/cocv/2022022 | |
Published online | 25 May 2022 |
Continuous feedback stabilization of nonlinear control systems by composition operators*
1
University of Wyoming, Department of Mathematics & Statistics, Laramie, WY, USA
2
Wayne State University, Department of Mathematics, Detroit, MI, USA
3
University of Minnesota, Department of Radiology, Minneapolis, MN, USA
** Corresponding author: aa1086@wayne.edu
Received:
31
January
2020
Accepted:
29
March
2022
The ability to asymptotically stabilize control systems through the use of continuous feedbacks is an important topic of control theory and applications. In this paper, we provide a complete characterization of continuous feedback stabilizability using a new approach that does not involve control Lyapunov functions. To do so, we first develop a slight generalization of feedback stabilization using composition operators and characterize continuous stabilizability in this expanded setting. Employing the obtained characterizations in the more general context, we establish relationships between continuous stabiliza|bility in the conventional sense and in the generalized composition operator sense. This connection allows us to show that the continuous stabilizability of a control system is equivalent to the stability of an associated system formed from a local section of the vector field inducing the control system. That is, we reduce the question of continuous stabilizability to that of stability. Moreover, we provide a universal formula describing all possible continuous stabilizing feedbacks for a given system.
Mathematics Subject Classification: 93D15 / 93D20 / 93C10 / 49J53
Key words: Nonlinear control systems / feedback stabilization / composition operators / asymptotic stabilizability / implicit function theorems
© The authors. Published by EDP Sciences, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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