Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications

Vesna Antoska Knights (), Olivera Petrovska and Jasenka Gajdoš Kljusurić
Additional contact information
Vesna Antoska Knights: Faculty of Technology and Technical Sciences, University St. Kliment Ohridski, 7000 Bitola, North Macedonia
Olivera Petrovska: Faculty of Technical Science, Mother Teresa University, 1000 Skopje, North Macedonia
Jasenka Gajdoš Kljusurić: Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia

Future Internet, 2024, vol. 16, issue 12, 1-23

Abstract: 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.

Keywords: nonlinear dynamics; machine learning; robotic control (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/12/435/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/12/435/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:12:p:435-:d:1526515

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().