An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks
Currently, artificial intelligence and intelligent algorithms for the control of dynamic systems are the main focus for building Industry 4.0 services and developing novel, innovative industrial solutions. This paper proposes a novel intelligent control structure specifically tailored for treating e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/11/5372 |
| _version_ | 1797494243388817408 |
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| author | Miroslav Milovanović Alexandru Oarcea Saša Nikolić Andjela Djordjević Miodrag Spasić |
| author_facet | Miroslav Milovanović Alexandru Oarcea Saša Nikolić Andjela Djordjević Miodrag Spasić |
| author_sort | Miroslav Milovanović |
| collection | DOAJ |
| description | Currently, artificial intelligence and intelligent algorithms for the control of dynamic systems are the main focus for building Industry 4.0 services and developing novel, innovative industrial solutions. This paper proposes a novel intelligent control structure specifically tailored for treating environmental stimuli and disturbances in operational environments of dynamic systems. The structure is based on the Orthogonal Endocrine Neural Network (OENN) and Artificial Orthogonal Glands (AOGs). The operational mechanism of each AOG acquires and processes environmental stimuli and generates artificial hormone concentration values at the gland output. These values are introduced to the appropriate OENN layer to provoke the network with collected environmental insights. To verify the applicability of the proposed structure on a complex dynamical nonlinear system, it was tested in a laboratory environment on the laboratory magnetic levitation system (MLS). The main experimental goal was to test the tracking performance of a levitation object when the new control logic was applied. The results were compared with two additional intelligent algorithms and a default linear quadratic (LQ) control logic. OENN + AOG structure showed improved tracking performances compared with traditional LQ control and better adaptability to environmental conditions compared with similar existing solutions. |
| first_indexed | 2024-03-10T01:31:33Z |
| format | Article |
| id | doaj.art-4f614675c4c2489285cd3b90dd862dfa |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:31:33Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4f614675c4c2489285cd3b90dd862dfa2023-11-23T13:40:35ZengMDPI AGApplied Sciences2076-34172022-05-011211537210.3390/app12115372An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural NetworksMiroslav Milovanović0Alexandru Oarcea1Saša Nikolić2Andjela Djordjević3Miodrag Spasić4Faculty of Electronic Engineering, Department of Control Systems, University of Niš, 18000 Niš, SerbiaFaculty of Automotive, Mechatronics and Mechanics, Department of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, RomaniaFaculty of Electronic Engineering, Department of Control Systems, University of Niš, 18000 Niš, SerbiaFaculty of Electronic Engineering, Department of Control Systems, University of Niš, 18000 Niš, SerbiaFaculty of Electronic Engineering, Department of Control Systems, University of Niš, 18000 Niš, SerbiaCurrently, artificial intelligence and intelligent algorithms for the control of dynamic systems are the main focus for building Industry 4.0 services and developing novel, innovative industrial solutions. This paper proposes a novel intelligent control structure specifically tailored for treating environmental stimuli and disturbances in operational environments of dynamic systems. The structure is based on the Orthogonal Endocrine Neural Network (OENN) and Artificial Orthogonal Glands (AOGs). The operational mechanism of each AOG acquires and processes environmental stimuli and generates artificial hormone concentration values at the gland output. These values are introduced to the appropriate OENN layer to provoke the network with collected environmental insights. To verify the applicability of the proposed structure on a complex dynamical nonlinear system, it was tested in a laboratory environment on the laboratory magnetic levitation system (MLS). The main experimental goal was to test the tracking performance of a levitation object when the new control logic was applied. The results were compared with two additional intelligent algorithms and a default linear quadratic (LQ) control logic. OENN + AOG structure showed improved tracking performances compared with traditional LQ control and better adaptability to environmental conditions compared with similar existing solutions.https://www.mdpi.com/2076-3417/12/11/5372artificial glandorthogonal functionendocrine neural networkcontrol logicmagnetic levitationnonlinear dynamical system |
| spellingShingle | Miroslav Milovanović Alexandru Oarcea Saša Nikolić Andjela Djordjević Miodrag Spasić An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks Applied Sciences artificial gland orthogonal function endocrine neural network control logic magnetic levitation nonlinear dynamical system |
| title | An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks |
| title_full | An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks |
| title_fullStr | An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks |
| title_full_unstemmed | An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks |
| title_short | An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks |
| title_sort | approach to networking a new type of artificial orthogonal glands within orthogonal endocrine neural networks |
| topic | artificial gland orthogonal function endocrine neural network control logic magnetic levitation nonlinear dynamical system |
| url | https://www.mdpi.com/2076-3417/12/11/5372 |
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