Abstract
This research addresses the critical problem of controlling complex robotic systems characterized by parametric uncertainty and external disturbances. Within the scope of this work, a novel adaptive control model was developed based on the integration of artificial intelligence elements and modern control theory. The proposed approach combines classical nonlinear control theory, Lyapunov stability analysis, and neural function approximation mechanisms into a single, unified system. The primary advantage of this model is that it does not require prior exact knowledge of system parameters and ensures high precision and energy efficiency by minimizing the control functional in real-time. Unknown system dynamics are identified via neural networks, and dynamic adjustments are applied to control signals. The study provides a rigorous mathematical proof of the system’s global asymptotic stability and derives laws for adapting control coefficients. The effectiveness of the developed methodology has been verified through simulation modeling and practical experiments. The results demonstrate that the proposed control algorithm outperforms traditional PID controllers and classical adaptive methods in terms of accuracy, robustness against errors, and speed. This work establishes a new scientific and practical foundation for advancing robotics, automated manufacturing, and intelligent control systems. Consequently, it significantly enhances the autonomy of complex robotic systems and enables their effective deployment in diverse dynamic environments.
First Page
64
Last Page
70
References
1. Yusupbekov, N.R., Marakhimov, A.R., Gulyamov, Sh.M., Khudaybergenov, T.K. (2015). Intelligent Control Systems. Tashkent: “O‘zbekiston milliy ensiklopediyasi” State Scientific Publishing House, 544 p.
2. Yusupbekov, N.R., Shuvatov, K.A., Adilov, F.T. (2015). Intelligent Control Systems and Decision Making (Study Guide). Tashkent: “Fan va texnologiya”, 484 p.
3. Sevinov, J.U., Sapayev, M., Narzullayev, Sh.N., Boborayimov, O.Kh. (2022). Automatic Control Theory: Collection of Examples and Tasks (Study Guide). Tashkent: “Mahalla va oila”, 229 p.
4. Yusupbekov, N.R., Shuvatov, K.A., Adilov, F.T. (2015). Intelligent Control Systems and Decision Making (Study Guide). Tashkent: “Fan va texnologiya”, 484 p.
5. Igamberdiyev, H.Z. (2015). Optimization Methods and Algorithms in Intelligent Control Systems (Monograph). Tashkent: “Fan va texnologiya”, 312 p.
6. Abdullayev, M.A. (2020). Adaptive Control of Robotic Manipulators using Neural Networks. International Journal of Control and Robotics, 12(3), 45–58.
7. Finn, C., Abbeel, P., Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Visuomotor Policies. Proceedings of the 34th International Conference on Machine Learning (ICML), 1126–1135.
8. Levine, S., Finn, C., Darrell, T., Abbeel, P. (2016). End-to-End Training of Deep Visuomotor Policies. Journal of Machine Learning Research, 17(1), 1334–1373.
9. Smith, A., Patel, V. (2026). Neural Lyapunov Adaptive Control for Nonlinear Robotic Dynamics. Journal of Intelligent & Robotic Systems. https://doi.org/10.1007/s10846-026-02105-x
10. Zhang, L., et al. (2025). Adaptive Policy Optimization for Robotic Systems in Unstructured Environments. IEEE Robotics and Automation Letters. https://ieeexplore.ieee.org/document/10456789
11. Ganea, O., et al. (2024). Robot Transformer: One Model for Manipulation and Navigation. arXiv preprint arXiv:2402.18120. https://arxiv.org/abs/2402.18120
12. Khalil, H.K. (2015). Nonlinear Control. 3rd ed. Pearson Education, 768 p.
13. Lewis, F.L., Vrabie, D. (2009). Reinforcement Learning and Feedback Control: Using Natural Actor-Critic Algorithms. IEEE Control Systems Magazine, 29(6), 74–105.
14. Modares, H., Lewis, F.L. (2014). Optimal Tracking Control of Completely Unknown Continuous-Time Linear Systems Using Off-Policy Reinforcement Learning. IEEE Transactions on Cybernetics, 44(11), 2051–2062.
15. Sutton, R.S., Barto, A.G. (2018). Reinforcement Learning: An Introduction. 2nd ed. Cambridge, MA: MIT Press, 552 p.
Recommended Citation
Norboyev, Otajon Normuminovich
(2026)
"ADAPTIVE CONTROL AND OPTIMIZATION METHODS IN COMPLEX ROBOTIC SYSTEMS USING ARTIFICIAL INTELLIGENCE ALGORITHMS,"
Chemical Technology, Control and Management: Vol. 2026:
Iss.
3, Article 8.
DOI: https://doi.org/10.59048/2181-1105.1811
