论利用神经网络控制加热器的方法

IF 0.5 Q4 PHYSICS, MULTIDISCIPLINARY

Optoelectronics Instrumentation and Data Processing Pub Date : 2024-07-29 DOI:10.3103/s8756699024700328

S. S. Abdurakipov, E. B. Butakov

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引用次数: 0

摘要

摘要研究了使用长短期记忆（LSTM）神经网络模拟 PID 控制器运行的可能性。在 Arduino 微控制器的基础上实现了一个可编程 PID 控制器，用于控制带有温度传感器的加热器。基于控制器数据训练的 LSTM 模型已经开发出来。结果表明，该神经网络模型准确地再现了控制器的运行，并能在数据处理时间更长但足够的条件下完全取代控制器。该模型可用作 PID 控制器异常运行的检测器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

On a Method of Controlling Heater by Using Neural Networks

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On a Method of Controlling Heater by Using Neural Networks

Abstract

The possibility of using a long short-term memory (LSTM) neural network has been studied to simulate the operation of a PID controller. A programmable PID controller has been realized to control a heater with a temperature sensor on the basis of an Arduino microcontroller. A LSTM model trained on the controller data has been developed. It is shown that the neural network model accurately reproduces the operation of the controller and can completely replace it under the condition of a much greater but sufficient data processing time. The applicability of this model as a detector of abnormal operation of the PID controller is shown.

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来源期刊

Optoelectronics Instrumentation and Data Processing PHYSICS, MULTIDISCIPLINARY-

CiteScore

1.00

自引率

50.00%

发文量

期刊介绍： The scope of Optoelectronics, Instrumentation and Data Processing encompasses, but is not restricted to, the following areas: analysis and synthesis of signals and images; artificial intelligence methods; automated measurement systems; physicotechnical foundations of micro- and optoelectronics; optical information technologies; systems and components; modelling in physicotechnical research; laser physics applications; computer networks and data transmission systems. The journal publishes original papers, reviews, and short communications in order to provide the widest possible coverage of latest research and development in its chosen field.