COGNITIVE AI–BASED SELF-OPTIMIZING ADAPTIVE CONTROL ALGORITHMS
Vol. 2 No. 02 (2026), Articles
Vol. 2 No. 02 (2026)

COGNITIVE AI–BASED SELF-OPTIMIZING ADAPTIVE CONTROL ALGORITHMS

Articles
Published 2026-02-15
Nodirbek Yusupbekov Rustambekovich
Tashkent state technical university named after Islam Karimov, Tashkent, Uzbekistan
Urinjon Choriev Bayramali ugli
Tashkent state technical university named after Islam Karimov, Tashkent, Uzbekistan
Abdulla Khudoyberdiev Shomurod ugli
Tashkent state technical university named after Islam Karimov, Tashkent, Uzbekistan
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Keywords

artificial intelligence, self-optimizing control, adaptive control, nonlinear systems, constraint handling, Lyapunov stability, online optimization.

How to Cite

Yusupbekov Rustambekovich, N., Choriev Bayramali ugli, U., & Khudoyberdiev Shomurod ugli, A. (2026). COGNITIVE AI–BASED SELF-OPTIMIZING ADAPTIVE CONTROL ALGORITHMS. INTERNATIONAL CONFERENCE ON SCIENCE AND INNOVATIVE RESEARCH, 2(02), 26–30. Retrieved from https://scienceinno.org/index.php/iccir/article/view/107
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Abstract

This paper presents a plant-agnostic framework for cognitive AI–driven self-optimizing adaptive control of uncertain nonlinear systems with practical constraints. The method combines a stability-compatible adaptive controller with a confidence-regulated online self-optimization loop that tunes internal settings using closed-loop performance feedback under a two-time-scale design. Lyapunov-based arguments support bounded updates and practical stability. Benchmark studies with disturbances, time-varying parameters, noise, and actuator saturation show improved tracking–effort trade-offs and reduced constraint-violation risk compared with representative baselines.

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References

[1] European Patent Application EP3907646A1, “Digital twin of twinned physical system,” 2021.

[2] N. R. Yusupbekov and U. B. Choriev, “Improving optimal-adaptive control based on structural modeling in distributed parameter chemical processes,” International Journal of Artificial Intelligence, vol. 5, no. 12, pp. 2379–2390, 2025, ISSN 2692-5206.

[3] F. Hinder et al., “One or two things we know about concept drift – A survey,” Frontiers in Artificial Intelligence, 2024, doi: 10.3389/frai.2024.1330258.

[4] R. He, G. Chen, “Data-driven digital twin technology for optimized control in process systems,”. 95, pp. 221–234, Dec. 2019, doi: 10.1016/j.isatra.2019.05.011.

[5] I. Errandonea, S. Beltrán, and S. Arrizabalaga, “Digital twin for maintenance: A literature review,” Computers in Industry, vol. 123, Art. no. 103316, 2020, doi: 10.1016/j.compind.2020.103316.

[6] F. Tao, B. Xiao, Q. Qi, J. Cheng, and P. Ji, “Digital twin modeling,” Journal of Manufacturing Systems, vol. 64, pp. 372–389, 2022, doi: 10.1016/j.jmsy.2022.06.015.

[7] U. B. Choriev, “Kognitiv modellar asosida adaptiv boshqaruv algoritmlarini ishlab chiqish,” International Journal of Science-Innovative Research, vol. 1, no. 3, pp. 113–129, Oct. 2025.