Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks
Artificial neural networks (ANN), an Artificial Intelligence (AI) technique, are both bio-inspired and nature-inspired models that mimic the operations of the human brain and the central nervous system that is capable of learning. This paper is based on a system that optimizes the performance of an...
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| Format: | Article |
| Language: | English |
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Akif AKGUL
2024-03-01
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| Series: | Chaos Theory and Applications |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3531375 |
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| author | Oduetse Matsebe David Mohammed Ezekiel Ravi Samikannu |
| author_facet | Oduetse Matsebe David Mohammed Ezekiel Ravi Samikannu |
| author_sort | Oduetse Matsebe |
| collection | DOAJ |
| description | Artificial neural networks (ANN), an Artificial Intelligence (AI) technique, are both bio-inspired and nature-inspired models that mimic the operations of the human brain and the central nervous system that is capable of learning. This paper is based on a system that optimizes the performance of an uncertain unmanned nonlinear Multi-Input Multi-Output (MIMO) aerodynamic plant called Twin Rotor MIMO System (TRMS). The pitch and yaw angles which are challenging to control and optimize in practice, are being used as the input to the Nonlinear Auto-Regressive with eXogenous (NARX) model, and eventually trained. The training features use the Matlab Deep Learning Toolbox. The NARX structure has its core in the neural networks’ architecture. Data is collected from the TRMS testbed which is used to train the network. ANN as a Hybrid intelligent control strategy of ANN in combination with Pattern Search and Genetic Algorithm, is then utilized to optimize the parameters of the neural networks. At the end it was validated, tested and the optimized system run in simulation and compared with other intelligent and conventional controllers, with the proposed controller outperforming them, giving a very fast tracking control, stable and optimal performance that satisfactorily met all our design requirements. |
| format | Article |
| id | doaj-art-75062eef40ec4cabb2080aaa9358819b |
| institution | Kabale University |
| issn | 2687-4539 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Akif AKGUL |
| record_format | Article |
| series | Chaos Theory and Applications |
| spelling | doaj-art-75062eef40ec4cabb2080aaa9358819b2025-01-23T18:20:09ZengAkif AKGULChaos Theory and Applications2687-45392024-03-0161516210.51537/chaos.13894091971Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural NetworksOduetse Matsebe0https://orcid.org/0000-0001-6052-7320David Mohammed Ezekiel1https://orcid.org/0000-0002-1922-0690Ravi Samikannu2https://orcid.org/0000-0002-6945-6562Botswana International University of Science & Technology (BIUST)Botswana International University of Science & Technology (BIUST)Botswana International University of Science and TechnologyArtificial neural networks (ANN), an Artificial Intelligence (AI) technique, are both bio-inspired and nature-inspired models that mimic the operations of the human brain and the central nervous system that is capable of learning. This paper is based on a system that optimizes the performance of an uncertain unmanned nonlinear Multi-Input Multi-Output (MIMO) aerodynamic plant called Twin Rotor MIMO System (TRMS). The pitch and yaw angles which are challenging to control and optimize in practice, are being used as the input to the Nonlinear Auto-Regressive with eXogenous (NARX) model, and eventually trained. The training features use the Matlab Deep Learning Toolbox. The NARX structure has its core in the neural networks’ architecture. Data is collected from the TRMS testbed which is used to train the network. ANN as a Hybrid intelligent control strategy of ANN in combination with Pattern Search and Genetic Algorithm, is then utilized to optimize the parameters of the neural networks. At the end it was validated, tested and the optimized system run in simulation and compared with other intelligent and conventional controllers, with the proposed controller outperforming them, giving a very fast tracking control, stable and optimal performance that satisfactorily met all our design requirements.https://dergipark.org.tr/en/download/article-file/3531375artificial neuralnetworknonlinear autoregressivewithexogenoustwin rotor mimosystemmulti-input multioutputaerodynamicunmannedhelicopter model |
| spellingShingle | Oduetse Matsebe David Mohammed Ezekiel Ravi Samikannu Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks Chaos Theory and Applications artificial neuralnetwork nonlinear autoregressivewithexogenous twin rotor mimosystem multi-input multioutput aerodynamic unmannedhelicopter model |
| title | Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks |
| title_full | Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks |
| title_fullStr | Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks |
| title_full_unstemmed | Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks |
| title_short | Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks |
| title_sort | improved set point tracking control of an unmanned aerodynamic mimo system using hybrid neural networks |
| topic | artificial neuralnetwork nonlinear autoregressivewithexogenous twin rotor mimosystem multi-input multioutput aerodynamic unmannedhelicopter model |
| url | https://dergipark.org.tr/en/download/article-file/3531375 |
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