ANALYSIS AND OPTIMIZATION OF INTELLIGENT CONTROL OF THE DRYING PROCESS UNDER UNCERTAINTY

Authors

• Azizbek Nodirbekovich Yusupbekov, Tashkent State Technical University named after Islam KarimovFollow
• Marufjon Kobuljonovich Shodiev, Tashkent State Technical University named after Islam KarimovFollow
• Khusniddin Mamarasul ugli Esonov, Termiz State University of Engineering and Agrotechnologies, TermizFollow

Abstract

This section proposes a multi-criteria Pareto-optimization algorithm, integrated with Herbert Simon's four-stage decision-making scheme (Intelligence–Design–Choice–Implementation) and an ANFIS-based adaptive controller, for simultaneously optimizing the mutually conflicting quality, energy, and time criteria of the wheat-drying process. The algorithm for constructing the set of Pareto-optimal solutions and selecting the compromise solution closest to the ideal point is presented step by step. The implementation of the ANFIS controller in the MATLAB/Simulink environment is comprehensively verified through a simulation block diagram, the Rule Viewer, the Surface Viewer, the training error-function graph, the RMSE convergence graph, and a learning-curve analysis. Training was carried out on a set of 1200 observations using a hybrid algorithm (least-squares method combined with gradient descent) over 150 epochs, yielding a final RMSE of approximately 0.14% (training) and 0.18% (validation). A comparative analysis of the classical PID controller, the static Fuzzy (Mamdani) controller, an artificial neural network (ANN), and the proposed ANFIS controller shows that ANFIS outperforms the other methods across all indicators (moisture content, control signal, temperature, and energy consumption).

First Page

39

Last Page

44

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Recommended Citation

Yusupbekov, Azizbek Nodirbekovich; Shodiev, Marufjon Kobuljonovich; and Esonov, Khusniddin Mamarasul ugli (2026) "ANALYSIS AND OPTIMIZATION OF INTELLIGENT CONTROL OF THE DRYING PROCESS UNDER UNCERTAINTY," Technical science and innovation: Vol. 2026: Iss. 3, Article 7.
Available at: https://btstu.researchcommons.org/journal/vol2026/iss3/7