Neuro-controller implementation for the embedded control system for mini-greenhouse
Control of a certain object can be implemented using different principles, namely, a certain software-implemented algorithm, fuzzy logic, neural networks, etc. In recent years, the use of neural networks for applications in control systems has become increasingly popular. However, their implementati...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-11-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1680.pdf |
| _version_ | 1827762170583056384 |
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| author | Vasyl Teslyuk Ivan Tsmots Natalia Kryvinska Taras Teslyuk Yurii Opotyak Mariana Seneta Roman Sydorenko |
| author_facet | Vasyl Teslyuk Ivan Tsmots Natalia Kryvinska Taras Teslyuk Yurii Opotyak Mariana Seneta Roman Sydorenko |
| author_sort | Vasyl Teslyuk |
| collection | DOAJ |
| description | Control of a certain object can be implemented using different principles, namely, a certain software-implemented algorithm, fuzzy logic, neural networks, etc. In recent years, the use of neural networks for applications in control systems has become increasingly popular. However, their implementation in embedded systems requires taking into account their limitations in performance, memory, etc. In this article, a neuro-controller for the embedded control system is proposed, which enables the processing of input technological data. A structure for the neuro-controller is proposed, which is based on the modular principle. It ensures rapid improvement of the system during its development. The neuro-controller functioning algorithm and data processing model based on artificial neural networks are developed. The neuro-controller hardware is developed based on the STM32 microcontroller, sensors and actuators, which ensures a low cost of implementation. The artificial neural network is implemented in the form of a software module, which allows us to change the neuro-controller function quickly. As a usage example, we considered STM32-based implementation of the control system for an intelligent mini-greenhouse. |
| first_indexed | 2024-03-11T10:26:03Z |
| format | Article |
| id | doaj.art-bf30277dfc1448369bc2d0e3d7af7a87 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-11T10:26:03Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-bf30277dfc1448369bc2d0e3d7af7a872023-11-15T15:05:12ZengPeerJ Inc.PeerJ Computer Science2376-59922023-11-019e168010.7717/peerj-cs.1680Neuro-controller implementation for the embedded control system for mini-greenhouseVasyl Teslyuk0Ivan Tsmots1Natalia Kryvinska2Taras Teslyuk3Yurii Opotyak4Mariana Seneta5Roman Sydorenko6Department of Automated Control Systems, Lviv Polytechnic National University, Lviv, UkraineDepartment of Automated Control Systems, Lviv Polytechnic National University, Lviv, UkraineDepartment of Information Management and Enterprise Systems, Comenius University in Bratislava, Bratislava, SlovakiaDepartment of Information Systems and Networks, Lviv Polytechnic National University, Lviv, UkraineDepartment of Automated Control Systems, Lviv Polytechnic National University, Lviv, UkraineDepartment of Automated Control Systems, Lviv Polytechnic National University, Lviv, UkraineDepartment of Automated Control Systems, Lviv Polytechnic National University, Lviv, UkraineControl of a certain object can be implemented using different principles, namely, a certain software-implemented algorithm, fuzzy logic, neural networks, etc. In recent years, the use of neural networks for applications in control systems has become increasingly popular. However, their implementation in embedded systems requires taking into account their limitations in performance, memory, etc. In this article, a neuro-controller for the embedded control system is proposed, which enables the processing of input technological data. A structure for the neuro-controller is proposed, which is based on the modular principle. It ensures rapid improvement of the system during its development. The neuro-controller functioning algorithm and data processing model based on artificial neural networks are developed. The neuro-controller hardware is developed based on the STM32 microcontroller, sensors and actuators, which ensures a low cost of implementation. The artificial neural network is implemented in the form of a software module, which allows us to change the neuro-controller function quickly. As a usage example, we considered STM32-based implementation of the control system for an intelligent mini-greenhouse.https://peerj.com/articles/cs-1680.pdfNeuro-controllerArtificial neural networkSTM32Control systemIntelligentmini-greenhouse |
| spellingShingle | Vasyl Teslyuk Ivan Tsmots Natalia Kryvinska Taras Teslyuk Yurii Opotyak Mariana Seneta Roman Sydorenko Neuro-controller implementation for the embedded control system for mini-greenhouse PeerJ Computer Science Neuro-controller Artificial neural network STM32 Control system Intelligentmini-greenhouse |
| title | Neuro-controller implementation for the embedded control system for mini-greenhouse |
| title_full | Neuro-controller implementation for the embedded control system for mini-greenhouse |
| title_fullStr | Neuro-controller implementation for the embedded control system for mini-greenhouse |
| title_full_unstemmed | Neuro-controller implementation for the embedded control system for mini-greenhouse |
| title_short | Neuro-controller implementation for the embedded control system for mini-greenhouse |
| title_sort | neuro controller implementation for the embedded control system for mini greenhouse |
| topic | Neuro-controller Artificial neural network STM32 Control system Intelligentmini-greenhouse |
| url | https://peerj.com/articles/cs-1680.pdf |
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