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COMBINATION OF MODEL-ADAPTIVE AND INVARIANT CONTROL IN HEAT POWER SYSTEMS

Field of Science:Artificial IntelligenceComputational Theory and MathematicsComputer Graphics and Computer-Aided DesignComputer Networks and CommunicationsInformation SystemsSignal ProcessingSoftware
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12._Khakimov_A.A_-_COMB....pdf

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Scientific electronic journal "Digital Economy"

Scientific electronic journal "Digital Economy"

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ARTICLE ANNOTATION

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The combination of model-adaptive and invariant control in heat power systems is used to ensure stability in processes with delayed dynamics and increase energy efficiency. The approach updates the system model through online parameter identification (e.g., RLS) and at the same time maintains resistance to model errors and external disturbances through a robust invariant element (robustness with H∞, sliding mode or LQR). Smith-predictor or model-based predictors are used to compensate for the delay, which, together with the parameters obtained from the adaptive estimator, allows for more accurate forecasting and control. Practical tests and simulations show that energy consumption, temperature deviation from the set profile, and recovery speed are the main evaluation criteria; a properly tuned coupling reduces energy losses while maintaining system stability, but requires careful selection of adaptation and robust component parameters.

AUTHORS

A.Khakimov

Farg'ona davlat texnika universiteti

Tags

# системы# теплоснабжения# адаптивное# управление# инвариантное

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Article Identifiers

ROI:

https://eroi.uz/11.142/A026-868190

DOI:

Not Available

References

1. Misri, M. E. (2025, February). Enhancing Cloud Data Security Using Paillier Homographic Encryption Algorithm. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-5). IEEE. 2. Kamalasadan, S., Swann, G. D., & Yousefian, R. (2013). A novel system centric intelligent adaptive control architecture for power system stabilizer based on adaptive neural networks. IEEE Systems Journal, 8(4), 1074-1085. 3. Misri, M. E. (2024, December). Binary Water Wheel Plant Algorithm with Soft Gated Recurrent Unit for Software Defect Prediction. In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1 5). IEEE. 4. Misri, M. E. The Impact Of Devops Practices On Software Development Lifecycle. 5. Carbone, M. A., & Loparo, K. A. (2023). Fault detection and diagnosis in spacecraft electrical power systems. Journal of Aerospace Information Systems, 20(6), 308-318. 6. Fathi, M., Bolandi, H., Vaghei, B. G., & Ebadollahi, S. (2025). On feasibility and asymptotically stability of switched systems using adaptive multi-model predictive control. Complex System Modeling and Simulation. 7. Carbone, M. A., & Loparo, K. A. (2023). Fault detection and diagnosis in spacecraft electrical power systems. Journal of Aerospace Information Systems, 20(6), 308-318. 8. Saadatmand, M., Gharehpetian, G. B., Moghassemi, A., Guerrero, J. M., Siano, P., & Alhelou, H. H. (2021). Damping of low-frequency oscillations in power systems by large-scale PV farms: A comprehensive review of control methods. IEEE Access, 9, 72183-72206.