Critical Growth for Stochastic Adaptive Stabilization with an Unknown Input Gain
Abstract
This paper studies adaptive stabilization of a class of scalar discrete-time nonlinear systems with unknown drift coefficients and an unknown nonzero input gain. Under iid Gaussian noise, we establish a necessary and sufficient polynomial criterion for global stabilizability, characterizing the admissible combinations of growth exponents. When the criterion holds, we construct a feedback combining finite calibration with adaptive control. The time average of the squared output converges almost surely to the noise variance, attaining the smallest possible value. If the criterion fails, then for every Borel causal feedback there exist fixed parameter values and an initial state for which the output magnitude diverges with positive probability.
Related
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BibTeX
@misc{liu2026critical,
title = {{Critical Growth for Stochastic Adaptive Stabilization with an Unknown Input Gain}},
author = {Zhaobo Liu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.26665},
eprint = {2610.26665},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.26665},
}
RIS
TY - UNPB
TI - Critical Growth for Stochastic Adaptive Stabilization with an Unknown Input Gain
AU - Zhaobo Liu
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.26665
UR - https://arxiv.org/abs/2610.26665
AB - This paper studies adaptive stabilization of a class of scalar discrete-time nonlinear systems with unknown drift coefficients and an unknown nonzero input gain. Under iid Gaussian noise, we establish a necessary and sufficient polynomial criterion for global stabilizability, characterizing the admissible combinations of growth exponents. When the criterion holds, we construct a feedback combining finite calibration with adaptive control. The time average of the squared output converges almost surely to the noise variance, attaining the smallest possible value. If the criterion fails, then for every Borel causal feedback there exist fixed parameter values and an initial state for which the output magnitude diverges with positive probability.
ER -