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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Journal of Engineering and Technological Sciences
Sekhar, Ravi
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

 1 
Robotics in Industry 4.0: A Bibliometric Analysis (2011-2022) Sekhar, Ravi; Shah, Pritesh; Iswanto, Iswanto
Journal of Robotics and Control (JRC) Vol 3, No 5 (2022): September
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i5.15453

Abstract

Robotics forms an integral part of industry 4.0, the industrial revolution of the 21st century. This paper presents a bibliometric analysis of Web of Science (WoS) indexed publications addressing this emerging field from 2011 till June 2022. WoS research publications were firstly analysed along multiple verticals such as annual counts, types, publishing sources, research directions, researchers, organizations, and countries. Next, co-authorship collaborations among authors, organizations, and countries were discovered. This was followed by an analysis of co-occurring keywords related to robotics in industry 4.0. Finally, a detailed citation analysis was carried out to unearth citation linkages among authors, institutions, documents, nations, and journals. Latest trends, under-investigated topics, and future directions are also discussed. Primary results indicate that more than 3000 articles are being published annually in this emerging field, with a total of 18,893 documents published in WoS during the last decade. The 'IEEE Access', Chinese Academy of Science, Wang Y. (USA), and the USA emerged as the topmost productive journal, institution, author, and nation. Porpiglia Francesco (Italy), Chinese Academy Science and USA obtained the highest co-authorship total link strength (TLS); whereas Lee Chengkuo (Singapore), China, Chinese Academy Science, and the IEEE Access scored the highest citation TLS among authors, countries, organizations, and sources respectively. Machine learning (ML) emerged as the highest co-occurring keyword, followed by artificial intelligence (AI). Computer Science emerged as the most trending research domain, followed by general applications. In the future, ML and AI will advance more sophisticated robots in industry 4.0 systems.
Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System D., Murugesan; K., Jagatheesan; Shah, Pritesh; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19772

Abstract

Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.
Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network Shihab, Mustafa Abdmajeed; Marhoon, Haydar Abdulameer; Ahmed, Saadaldeen Rashid; Radhi, Ahmed Dheyaa; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.23214

Abstract

Adversarial attacks represent a substantial threat to the security and reliability of machine learning models employed in wireless sensor networks (WSNs). This study tries to solve this difficulty by evaluating the efficiency of different defensive mechanisms in minimizing the effects of evasion assaults, which try to mislead ML models into misclassification. We employ the Edge-IIoTset dataset, a comprehensive cybersecurity dataset particularly built for IoT and IIoT applications, to train and assess our models. Our study reveals that employing adversarial training, robust optimization, and feature transformations dramatically enhances the resistance of machine learning models against evasion attempts. Specifically, our defensive model obtains a significant accuracy boost of 12% compared to baseline models. Furthermore, we study the possibilities of combining alternative generative adversarial networks (GANs), random forest ensembles, and hybrid techniques to further boost model resilience against a broader spectrum of adversarial assaults. This study underlines the need for proactive methods in preserving machine learning systems in real-world WSN contexts and stresses the need for continued research and development in this quickly expanding area.
Enhanced Dynamic Control of Quadcopter PMSMs Using an ILQR-PCC System for Improved Stability and Reduced Torque Ripples Saleh, Ziyaad H.; Mejbel, Basim Ghalib; Radhi, Ahmed Dheyaa; Hashim, Abdulghafor Mohammed; Taha, Taha A.; Gökşenli, Nurettin; Hussain, Abadal-Salam T.; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23159

Abstract

Quadcopter technology has developed fast because it’s flexibility and capacity for high maneuvers. What makes PMSM suitable for Quadcopter is their high power to weight ratio, reliability and efficiency. These motors allow the operation of torque and speed control which are important for stability and maneuverability in the flight of the aircraft. Nevertheless, certain and smooth flight caused by regulation of PMSM speed and current is necessary for stable and maneuverable movement. This work presents a new control strategy connecting the ILQR control to govern the speed while the PCC profit the dynamic response and control torque ripples. A comparison is made on the performance of the ILQR-PCC system with nominal Proportional-Integral (PI) control and ILQR.  From the results it is evident that the ILQR-PCC system is far superior to both the PI ILQR controller in regards to the dynamic response, the disturbances rejection capacity as well as reducing the current signal distortions hence reducing the torque ripples. Its working was evidenced in a nonlinear LQR-controlled quadcopter to track the reference accurately and to have minimum distortion in current regulation. The presented work improves the control systems of quadcopters: it introduces a reliable method that improves stability and increases the performance of the quadcopter; therefore, this paper contributes to the existing knowledge.
Enhanced Dynamic Control of Quadcopter PMSMs Using an ILQR-PCC System for Improved Stability and Reduced Torque Ripples Saleh, Ziyaad H.; Mejbel, Basim Ghalib; Radhi, Ahmed Dheyaa; Hashim, Abdulghafor Mohammed; Taha, Taha A.; Gökşenli, Nurettin; Hussain, Abadal-Salam T.; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23159

Abstract

Quadcopter technology has developed fast because it’s flexibility and capacity for high maneuvers. What makes PMSM suitable for Quadcopter is their high power to weight ratio, reliability and efficiency. These motors allow the operation of torque and speed control which are important for stability and maneuverability in the flight of the aircraft. Nevertheless, certain and smooth flight caused by regulation of PMSM speed and current is necessary for stable and maneuverable movement. This work presents a new control strategy connecting the ILQR control to govern the speed while the PCC profit the dynamic response and control torque ripples. A comparison is made on the performance of the ILQR-PCC system with nominal Proportional-Integral (PI) control and ILQR.  From the results it is evident that the ILQR-PCC system is far superior to both the PI & ILQR controller in regards to the dynamic response, the disturbances rejection capacity as well as reducing the current signal distortions hence reducing the torque ripples. Its working was evidenced in a nonlinear LQR-controlled quadcopter to track the reference accurately and to have minimum distortion in current regulation. The presented work improves the control systems of quadcopters: it introduces a reliable method that improves stability and increases the performance of the quadcopter; therefore, this paper contributes to the existing knowledge.
Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network Shihab, Mustafa Abdmajeed; Marhoon, Haydar Abdulameer; Ahmed, Saadaldeen Rashid; Radhi, Ahmed Dheyaa; Sekhar, Ravi
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.23214

Abstract

Adversarial attacks represent a substantial threat to the security and reliability of machine learning models employed in wireless sensor networks (WSNs). This study tries to solve this difficulty by evaluating the efficiency of different defensive mechanisms in minimizing the effects of evasion assaults, which try to mislead ML models into misclassification. We employ the Edge-IIoTset dataset, a comprehensive cybersecurity dataset particularly built for IoT and IIoT applications, to train and assess our models. Our study reveals that employing adversarial training, robust optimization, and feature transformations dramatically enhances the resistance of machine learning models against evasion attempts. Specifically, our defensive model obtains a significant accuracy boost of 12% compared to baseline models. Furthermore, we study the possibilities of combining alternative generative adversarial networks (GANs), random forest ensembles, and hybrid techniques to further boost model resilience against a broader spectrum of adversarial assaults. This study underlines the need for proactive methods in preserving machine learning systems in real-world WSN contexts and stresses the need for continued research and development in this quickly expanding area.
Co-Authors Abdul Wahab, Noor Izzri Ahmed, Ahmed H. Ahmed, Omer K. Ahmed, Saadaldeen Rashid Ahmed, Shouket A. Almalaisi, Taha Abdulsalam Awad, Raad Hamza Bandyopadhyay, Debjyoti Bektaş, Enes Çiçek, Adem D., Murugesan Francis, Sandra Ganorkar, Hiranmayee Gökşenli, Nurettin Hamad, Hasan Salman Hashim, Abdulghafor Mohammed Hassan, Mohd Khair Hussain, Abadal-Salam T. Iswanto Iswanto Itankar, Nilisha K., Jagatheesan Majdi, Hasan S. Marhoon, Haydar Abdulameer Mejbel, Basim Ghalib Radhi, Ahmed Dheyaa Rairikar, Sandeep Rairikar, Sandeep D Saleh, Ziyaad H. Shah, Pritesh Shihab, Mustafa Abdmajeed Singh, Abhaya Pal Solke, Nitin Sonawane, Shailesh Sonawane, Shailesh B Sutar, Prasanna S Taha, Taha A. Thipse, Sukrut Thipse, Sukrut S Varma, Chetan Warke, Arundhati Yahia, Elaf Hamzah Yassin, Khalil Farhan Zaynal, Hussein I.