Proceedings of International Conference on Applied Innovation in IT  ·  2026/07/22  ·  Vol. 14  ·  Issue 4  ·  pp. 1387–1394
Robust Assessment of the Degree of Influence of Identification Errors on the Dynamic Properties of a Process Model in a Multilevel Control System
Husan Igamberdiev, Uktam Mamirov, Latafat Gardashova, Bunyod Buronov and Shakhzoda Tillaeva
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Abstract
Assessing the impact of identification errors on the dynamic properties of multi-level objects under unmeasured disturbances involves analyzing deviations in model parameters and their impact on the system’s stability and accuracy. Errors at a single level (e.g., the lower control level) can cascade down the dynamics of the entire multi-level structure, especially if the disturbance cannot be measured and compensated for directly. This article analyzes the impact of identification errors on a model obtained using the least-squares method. A model of statistical errors in parameter estimation is proposed, and a probabilistically representative set of process models is defined. The estimated parameter vector is significantly amplified by small singular values, making the least-squares method extremely unreliable and impossible to obtain an accurate parameter estimate. To improve the stability of the estimate, Tikhonov’s regularization method was used, which adds a robust functional constraint to the classical least-squares correction criterion and introduces a regularization factor to regulate the balance. The results of this study, i.e., an assessment of the dynamic characteristics of the process model using a representative set, allow us to determine the upper limit of the amplitude-frequency response and obtain an estimate of the lower limit.
Keywords
Multilevel Dynamic Object Impact Assessment Dynamic Properties Identification Error Unmeasurable Disturbance Model Sensitivity
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