A Simple Soft Computing Structure for Modeling and Control
Using the interpolation/extrapolation skills of the core function of an iterative adaptive controller, a structurally simple single essential layer neural network-based topological structure is suggested with fast and explicit single-step teaching and data-retrieving abilities. Its operation does no...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/9/8/168 |
| _version_ | 1827684910391885824 |
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| author | Hemza Redjimi József Kázmér Tar |
| author_facet | Hemza Redjimi József Kázmér Tar |
| author_sort | Hemza Redjimi |
| collection | DOAJ |
| description | Using the interpolation/extrapolation skills of the core function of an iterative adaptive controller, a structurally simple single essential layer neural network-based topological structure is suggested with fast and explicit single-step teaching and data-retrieving abilities. Its operation does not assume massive parallelism, therefore it easily can be simulated by simple sequential program codes not needing sophisticated data synchronization mechanisms. It seems to be advantageous in approximate model-based common, robust, or adaptive controllers that can compensate for the effects of minor modeling imprecisions. In this structure a neuron can be in either a firing or a passive (i.e., producing zero output) state. In firing state its activation function realizes an abstract rotation that maps the desired kinematic data into the space of the necessary control forces. The activation function allows the use of a simple and fast incremental model modification for slowly varying dynamic models. Its operation is exemplified by numerical simulations for a van der Pol oscillator in free motion, and within a Computed Torque type control. To reveal the possibility for efficient model correction, a robust Variable Structure/Sliding Mode Controller is applied, too. The novel structure can be obtained by approximate experimental observations as e.g., the fuzzy models. |
| first_indexed | 2024-03-10T08:38:45Z |
| format | Article |
| id | doaj.art-143e30c378ba447187dc5c4e46670864 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T08:38:45Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-143e30c378ba447187dc5c4e466708642023-11-22T08:24:40ZengMDPI AGMachines2075-17022021-08-019816810.3390/machines9080168A Simple Soft Computing Structure for Modeling and ControlHemza Redjimi0József Kázmér Tar1Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Bécsi út 96/B, H-1034 Budapest, HungaryDoctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Bécsi út 96/B, H-1034 Budapest, HungaryUsing the interpolation/extrapolation skills of the core function of an iterative adaptive controller, a structurally simple single essential layer neural network-based topological structure is suggested with fast and explicit single-step teaching and data-retrieving abilities. Its operation does not assume massive parallelism, therefore it easily can be simulated by simple sequential program codes not needing sophisticated data synchronization mechanisms. It seems to be advantageous in approximate model-based common, robust, or adaptive controllers that can compensate for the effects of minor modeling imprecisions. In this structure a neuron can be in either a firing or a passive (i.e., producing zero output) state. In firing state its activation function realizes an abstract rotation that maps the desired kinematic data into the space of the necessary control forces. The activation function allows the use of a simple and fast incremental model modification for slowly varying dynamic models. Its operation is exemplified by numerical simulations for a van der Pol oscillator in free motion, and within a Computed Torque type control. To reveal the possibility for efficient model correction, a robust Variable Structure/Sliding Mode Controller is applied, too. The novel structure can be obtained by approximate experimental observations as e.g., the fuzzy models.https://www.mdpi.com/2075-1702/9/8/168soft computingneural networksfuzzy systemsadaptive controlrobust controlfixed-point iteration-based adaptive control |
| spellingShingle | Hemza Redjimi József Kázmér Tar A Simple Soft Computing Structure for Modeling and Control Machines soft computing neural networks fuzzy systems adaptive control robust control fixed-point iteration-based adaptive control |
| title | A Simple Soft Computing Structure for Modeling and Control |
| title_full | A Simple Soft Computing Structure for Modeling and Control |
| title_fullStr | A Simple Soft Computing Structure for Modeling and Control |
| title_full_unstemmed | A Simple Soft Computing Structure for Modeling and Control |
| title_short | A Simple Soft Computing Structure for Modeling and Control |
| title_sort | simple soft computing structure for modeling and control |
| topic | soft computing neural networks fuzzy systems adaptive control robust control fixed-point iteration-based adaptive control |
| url | https://www.mdpi.com/2075-1702/9/8/168 |
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