Self-Learning Control under Practical Actuation
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Abstract
This paper develops a general and implementation friendly stability framework for self-learning control (SLC) laws of the form $u(t)=k_1(t)u(t-\tau)+k_2 v(t)$. Uniform ultimate boundedness is established for a class of general linear plants under two engineering actuator assumptions: (i) smoothness and (ii) saturation with maximum value. These assumptions are reasonable for practical systems and yield an explicit bound, which converts the delay learning mechanism into a nominal (delay-free) controller plus a bounded perturbation injection. The most notable feature of SLC is its simplicity of structure coupled with excellent performance. It is compatible with traditional algorithms and can enhance even PD-type controllers. A complete design procedure and a spacecraft attitude tracking simulation example are provided to demonstrate compatibility with aerospace applications while presenting a broadly applicable theoretical result.
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References
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Cite This Article
TY - JOUR AU - Zhang, Chengxi PY - 2026 DA - 2026/01/29 TI - Self-Learning Control under Practical Actuation JO - Aerospace Engineering Communications T2 - Aerospace Engineering Communications JF - Aerospace Engineering Communications VL - 1 IS - 1 SP - 36 EP - 46 DO - 10.62762/AEC.2025.320719 UR - https://www.icck.org/article/abs/AEC.2025.320719 KW - self-learning control KW - uniform ultimate boundedness KW - smooth saturated actuator KW - spacecraft attitude control AB - This paper develops a general and implementation friendly stability framework for self-learning control (SLC) laws of the form $u(t)=k_1(t)u(t-\tau)+k_2 v(t)$. Uniform ultimate boundedness is established for a class of general linear plants under two engineering actuator assumptions: (i) smoothness and (ii) saturation with maximum value. These assumptions are reasonable for practical systems and yield an explicit bound, which converts the delay learning mechanism into a nominal (delay-free) controller plus a bounded perturbation injection. The most notable feature of SLC is its simplicity of structure coupled with excellent performance. It is compatible with traditional algorithms and can enhance even PD-type controllers. A complete design procedure and a spacecraft attitude tracking simulation example are provided to demonstrate compatibility with aerospace applications while presenting a broadly applicable theoretical result. SN - 3071-1967 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Zhang2026SelfLearni,
author = {Chengxi Zhang},
title = {Self-Learning Control under Practical Actuation},
journal = {Aerospace Engineering Communications},
year = {2026},
volume = {1},
number = {1},
pages = {36-46},
doi = {10.62762/AEC.2025.320719},
url = {https://www.icck.org/article/abs/AEC.2025.320719},
abstract = {This paper develops a general and implementation friendly stability framework for self-learning control (SLC) laws of the form \$u(t)=k\_1(t)u(t-\tau)+k\_2 v(t)\$. Uniform ultimate boundedness is established for a class of general linear plants under two engineering actuator assumptions: (i) smoothness and (ii) saturation with maximum value. These assumptions are reasonable for practical systems and yield an explicit bound, which converts the delay learning mechanism into a nominal (delay-free) controller plus a bounded perturbation injection. The most notable feature of SLC is its simplicity of structure coupled with excellent performance. It is compatible with traditional algorithms and can enhance even PD-type controllers. A complete design procedure and a spacecraft attitude tracking simulation example are provided to demonstrate compatibility with aerospace applications while presenting a broadly applicable theoretical result.},
keywords = {self-learning control, uniform ultimate boundedness, smooth saturated actuator, spacecraft attitude control},
issn = {3071-1967},
publisher = {Institute of Central Computation and Knowledge}
}
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Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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