Mechatronic Device Control by Artificial Intelligence
Nowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article pre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/13/5872 |
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| author | Martin Bohušík Vladimír Stenchlák Miroslav Císar Vladimír Bulej Ivan Kuric Tomáš Dodok Andrej Bencel |
| author_facet | Martin Bohušík Vladimír Stenchlák Miroslav Císar Vladimír Bulej Ivan Kuric Tomáš Dodok Andrej Bencel |
| author_sort | Martin Bohušík |
| collection | DOAJ |
| description | Nowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article presents the use of artificial intelligence (prediction) for the control of three motors used for effector control in a spherical parallel kinematic structure of a designed device. The kinematic model used was the “Agile eye” which can achieve high dynamics and has three degrees of freedom. A prototype of this device was designed and built, on which experiments were carried out in the framework of motor control. As the prototype was created through the means of the available equipment (3D printing and lathe), the clearances of the kinematic mechanism were made and then calibrated through prediction. The paper also presents a method for motor control calibration. On the one hand, using AI is an efficient way to achieve higher precision in positioning the optical axis of the effector. On the other hand, such calibration would be rendered unnecessary if the clearances and inaccuracies in the mechanism could be eliminated mechanically. The device was designed with imperfections such as clearances in mind so the effectiveness of the calibration could be tested and evaluated. The resulting control of the achieved movements of the axis of the device (effector) took place when obtaining the exact location of the tracked point. There are several methods for controlling the motors of mechatronic devices (e.g., Matlab-Simscape). This paper presents an experiment performed to verify the possibility of controlling the kinematic mechanism through neural networks and eliminating inaccuracies caused by imprecisely produced mechanical parts. |
| first_indexed | 2024-03-11T01:29:25Z |
| format | Article |
| id | doaj.art-721746768b3f4ccf82dac15dfa7ed040 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:25Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-721746768b3f4ccf82dac15dfa7ed0402023-11-18T17:28:03ZengMDPI AGSensors1424-82202023-06-012313587210.3390/s23135872Mechatronic Device Control by Artificial IntelligenceMartin Bohušík0Vladimír Stenchlák1Miroslav Císar2Vladimír Bulej3Ivan Kuric4Tomáš Dodok5Andrej Bencel6Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, SlovakiaNowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article presents the use of artificial intelligence (prediction) for the control of three motors used for effector control in a spherical parallel kinematic structure of a designed device. The kinematic model used was the “Agile eye” which can achieve high dynamics and has three degrees of freedom. A prototype of this device was designed and built, on which experiments were carried out in the framework of motor control. As the prototype was created through the means of the available equipment (3D printing and lathe), the clearances of the kinematic mechanism were made and then calibrated through prediction. The paper also presents a method for motor control calibration. On the one hand, using AI is an efficient way to achieve higher precision in positioning the optical axis of the effector. On the other hand, such calibration would be rendered unnecessary if the clearances and inaccuracies in the mechanism could be eliminated mechanically. The device was designed with imperfections such as clearances in mind so the effectiveness of the calibration could be tested and evaluated. The resulting control of the achieved movements of the axis of the device (effector) took place when obtaining the exact location of the tracked point. There are several methods for controlling the motors of mechatronic devices (e.g., Matlab-Simscape). This paper presents an experiment performed to verify the possibility of controlling the kinematic mechanism through neural networks and eliminating inaccuracies caused by imprecisely produced mechanical parts.https://www.mdpi.com/1424-8220/23/13/5872agile eyepredictionartificial intelligenceneural networks |
| spellingShingle | Martin Bohušík Vladimír Stenchlák Miroslav Císar Vladimír Bulej Ivan Kuric Tomáš Dodok Andrej Bencel Mechatronic Device Control by Artificial Intelligence Sensors agile eye prediction artificial intelligence neural networks |
| title | Mechatronic Device Control by Artificial Intelligence |
| title_full | Mechatronic Device Control by Artificial Intelligence |
| title_fullStr | Mechatronic Device Control by Artificial Intelligence |
| title_full_unstemmed | Mechatronic Device Control by Artificial Intelligence |
| title_short | Mechatronic Device Control by Artificial Intelligence |
| title_sort | mechatronic device control by artificial intelligence |
| topic | agile eye prediction artificial intelligence neural networks |
| url | https://www.mdpi.com/1424-8220/23/13/5872 |
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