基于神经网络逆模型的制冷系统解耦控制

doi:10.16180/j.cnki.issn1007-7820.2025.07.011

Abstract

Abstract:

In view of the nonlinearity and multi-variable coupling of compression refrigeration system, the inverse system control method of α-order neural network is used to decouple it into two first-order subsystems:superheat and evaporation temperature. On this basis, the linear closed-loop controller PID(Proportional Integration Differentiation) is added to realize the high performance decoupling control of the system. The results show that the proposed method is simple in structure and easy to implement, and effectively avoids the shortcomings of the traditional control method which depends on the accuracy of the system model. The step response time for both superheat and evaporation temperature is reduced by 234 s and 360 s,respectively. The overshoot of the evaporation temperature and superheat under step perturbation is decreased by 9.4% and 13.3%,respectively, demonstrating that the proposed method displays better dynamic performance and stability.

Key words: refrigeration system, evaporator, superheat, evaporation temperature, inverse system, RBF neural network, PID, decoupling

CLC Number:

TP273

WANG Junchao, DING Xudong, YANG Yuanxing, LIU Yuting, YANG Yuping. Research on Decoupling Control of Refrigeration System Based on Neural Network Inverse Model[J].Electronic Science and Technology, 2025, 38(7): 82-88.

Figures/Tables 15

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Research on Decoupling Control of Refrigeration System Based on Neural Network Inverse Model

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