Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications
This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/16/12/435 |
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| author | Vesna Antoska Knights Olivera Petrovska Jasenka Gajdoš Kljusurić |
| author_facet | Vesna Antoska Knights Olivera Petrovska Jasenka Gajdoš Kljusurić |
| author_sort | Vesna Antoska Knights |
| collection | DOAJ |
| description | This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments and the importance of machine learning. The proposed hybrid control system is designed for a 20 degrees of freedom (DOFs) robotic platform, combining traditional nonlinear control methods with machine learning models to predict and optimize robotic movements. The machine learning models, including neural networks, are trained using historical data and real-time sensor inputs to dynamically adjust the control parameters. Through simulations, the system demonstrated improved accuracy in trajectory tracking and adaptability, particularly in nonlinear and time-varying environments. The results show that combining traditional control strategies with machine learning significantly enhances the robot’s performance in real-world scenarios. This work offers a foundation for future research into intelligent control systems, with broader implications for industrial applications where precision and adaptability are critical. |
| format | Article |
| id | doaj-art-71f0d7c205364d5998bddd2775c22728 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-71f0d7c205364d5998bddd2775c227282024-12-27T14:27:17ZengMDPI AGFuture Internet1999-59032024-11-01161243510.3390/fi16120435Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT ApplicationsVesna Antoska Knights0Olivera Petrovska1Jasenka Gajdoš Kljusurić2Faculty of Technology and Technical Sciences, University St. Kliment Ohridski, 7000 Bitola, North MacedoniaFaculty of Technical Science, Mother Teresa University, 1000 Skopje, North MacedoniaFaculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaThis paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments and the importance of machine learning. The proposed hybrid control system is designed for a 20 degrees of freedom (DOFs) robotic platform, combining traditional nonlinear control methods with machine learning models to predict and optimize robotic movements. The machine learning models, including neural networks, are trained using historical data and real-time sensor inputs to dynamically adjust the control parameters. Through simulations, the system demonstrated improved accuracy in trajectory tracking and adaptability, particularly in nonlinear and time-varying environments. The results show that combining traditional control strategies with machine learning significantly enhances the robot’s performance in real-world scenarios. This work offers a foundation for future research into intelligent control systems, with broader implications for industrial applications where precision and adaptability are critical.https://www.mdpi.com/1999-5903/16/12/435nonlinear dynamicsmachine learningrobotic control |
| spellingShingle | Vesna Antoska Knights Olivera Petrovska Jasenka Gajdoš Kljusurić Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications Future Internet nonlinear dynamics machine learning robotic control |
| title | Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications |
| title_full | Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications |
| title_fullStr | Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications |
| title_full_unstemmed | Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications |
| title_short | Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications |
| title_sort | nonlinear dynamics and machine learning for robotic control systems in iot applications |
| topic | nonlinear dynamics machine learning robotic control |
| url | https://www.mdpi.com/1999-5903/16/12/435 |
| work_keys_str_mv | AT vesnaantoskaknights nonlineardynamicsandmachinelearningforroboticcontrolsystemsiniotapplications AT oliverapetrovska nonlineardynamicsandmachinelearningforroboticcontrolsystemsiniotapplications AT jasenkagajdoskljusuric nonlineardynamicsandmachinelearningforroboticcontrolsystemsiniotapplications |