Intelligent Controller Design by the Artificial Intelligence Methods
With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/16/4454 |
| _version_ | 1797559228269854720 |
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| author | Jana Nowaková Miroslav Pokorný |
| author_facet | Jana Nowaková Miroslav Pokorný |
| author_sort | Jana Nowaková |
| collection | DOAJ |
| description | With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable. |
| first_indexed | 2024-03-10T17:42:27Z |
| format | Article |
| id | doaj.art-0f9123110a71463eb50a87cd790bdff0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:42:27Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0f9123110a71463eb50a87cd790bdff02023-11-20T09:38:28ZengMDPI AGSensors1424-82202020-08-012016445410.3390/s20164454Intelligent Controller Design by the Artificial Intelligence MethodsJana Nowaková0Miroslav Pokorný1Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 33 Ostrava – Poruba, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 33 Ostrava – Poruba, Czech RepublicWith the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.https://www.mdpi.com/1424-8220/20/16/4454intelligent controllerPID controllerartificial intelligenceexpert systemsfuzzy methodsgenetic algorithms |
| spellingShingle | Jana Nowaková Miroslav Pokorný Intelligent Controller Design by the Artificial Intelligence Methods Sensors intelligent controller PID controller artificial intelligence expert systems fuzzy methods genetic algorithms |
| title | Intelligent Controller Design by the Artificial Intelligence Methods |
| title_full | Intelligent Controller Design by the Artificial Intelligence Methods |
| title_fullStr | Intelligent Controller Design by the Artificial Intelligence Methods |
| title_full_unstemmed | Intelligent Controller Design by the Artificial Intelligence Methods |
| title_short | Intelligent Controller Design by the Artificial Intelligence Methods |
| title_sort | intelligent controller design by the artificial intelligence methods |
| topic | intelligent controller PID controller artificial intelligence expert systems fuzzy methods genetic algorithms |
| url | https://www.mdpi.com/1424-8220/20/16/4454 |
| work_keys_str_mv | AT jananowakova intelligentcontrollerdesignbytheartificialintelligencemethods AT miroslavpokorny intelligentcontrollerdesignbytheartificialintelligencemethods |