基于Smith预估器的制冷系统BP神经网络PID控制算法

doi:10.16180/j.cnki.issn1007-7820.2025.08.006

Abstract

Abstract:

In view of the problems of large time delay, high coupling, nonlinearity and external interference in the actual operation of compression refrigeration system, a BP(Back Propagation) neural network PID(Proponential Integration Differentiation) control algorithm based on Smith predictor is proposed in this study. Smith predictor compensator is used to predict and compensate the actual output of the system, and its predictive compensation mechanism is used to eliminate the delay link of the system and alleviate the influence of time delay on the system. The self-learning ability of BP neural network is used to decouple the compressed refrigeration system into two independent loop systems, and PID parameters are adjusted to cope with the changes of the system and external interference. MATLAB simulation results show that the proposed control strategy has obvious advantages in improving the dynamic performance and anti-interference performance of refrigeration system. The adjustment time of superheat and evaporation temperature is reduced by 123 s and 204 s, and the overshoot is reduced by 5.27% and 10.22%. And it has good robustness under the condition of changing parameters, and also reduces the overshoot of control, which provides an effective control scheme for the stable operation of compression refrigeration system.

Key words: compression refrigeration system, model identification, Smith predictor, PID, multivariate decoupling, BP neural network, MATLAB simulation, decoupling control

CLC Number:

TP273

YANG Yuanxing, DING Xudong, WANG Junchao, WU Dong. Research on BP Neural Network PID Control Algorithm of Refrigeration System Based on Smith Predictor[J].Electronic Science and Technology, 2025, 38(8): 42-48.

Figures/Tables 6

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References 20

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Research on BP Neural Network PID Control Algorithm of Refrigeration System Based on Smith Predictor

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