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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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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.
Experimental Investigation and Prediction of Combustion Parameters using Machine Learning in Ethanol - Gasoline Blended Engines Sonawane, Shailesh; Sekhar, Ravi; Warke, Arundhati; Thipse, Sukrut; Rairikar, Sandeep; Varma, Chetan
Journal of Engineering and Technological Sciences Vol. 57 No. 1 (2025): Vol. 57 No. 1 (2025): February
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.1.3

Abstract

Alternative fuels play an important role in eco-friendly transport solutions. Wider adoption of alternative blended fuels in automobiles is dependent on a better understanding of the blended fuel engine characteristics. This paper presents an experimental investigation on the part load combustion characteristics of a multi cylinder spark ignition (SI) engine fueled by E0 and E10 ethanol blends. Full factorial Taguchi experimental design was employed to include multi-level engine speed (rpm) and load (throttle %) variations. High-speed data acquisition was used to record combustion parameters viz. maximum pressure (Pmax), indicative mean effective pressure (IMEP), start of combustion (SOC), mass burn fraction (MBF) and burn duration (Brn_drn) over 300 combustion cycles for each experimental run. Grey Relational Analysis (GRA) was used to determine the optimum best and worst engine operating conditions based on Pmax, IMEP, MBF and Brn_drn. Cycle-to-cycle variations of Pmax were also examined in detail to identify the worst engine operating condition. Random Forest machine learning algorithm was employed to accurately model Pmax and SOC in terms of the engine part load operating conditions. This model can be used to predict Pmax and SOC characteristics of an E0/E10 fueled SI engine under different operating conditions, eliminating the need for extensive testing
Optimized Selective Harmonic Elimination in CHB-MLI Using Red-Tailed Hawk Algorithm for Unequal DC Sources Yahia, Elaf Hamzah; Hamad, Hasan Salman; Ahmed, Shouket A.; Almalaisi, Taha Abdulsalam; Majdi, Hasan S.; Ahmed, Omer K.; Solke, Nitin; Sekhar, Ravi
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1777

Abstract

The study develops an optimized SHE procedure to regulate a CHB-MLI powered by PV modules which use unequal DC sources. The main goal involves finding suitable switching angles that produce minimal low-order harmonics during steady output voltage operation under variable input scenarios. The Red-Tailed Hawk Algorithm (RTHA) serves as a recent bio-inspired metaheuristic optimization method to solve effectively the nonlinear transcendental SHE equations. The MATLAB/Simulink environment implements a validation of the proposed method by modeling a three-phase 7-level CHB-MLI system. A performance evaluation of the proposed algorithm occurs against established optimization methods consisting of Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). Total Harmonic Distortion reduction, computational efficiency and convergence rate serve as the three main performance indicators for evaluation. The experimental findings show RTHA accomplishes higher harmonic reduction while offering improved speed and stability when dealing with unequal DC voltage issues when contrasted against traditional optimization methods. RTHA operates better than analytical approaches in real-world inverter applications through its flexible and adaptable approach despite needing complex calculations and preset conditions. The scale-up of RTHA applications requires additional research because excessive computational requirements and initial value dependencies must be addressed. The research shows that RTHA-based SHE optimization represents a viable and implementable solution for power quality advancement in renewable energy systems.
Optimization of Harmonic Elimination in PV-Fed Asymmetric Multilevel Inverters Using Evolutionary Algorithms Almalaisi, Taha Abdulsalam; Abdul Wahab, Noor Izzri; Zaynal, Hussein I.; Hassan, Mohd Khair; Majdi, Hasan S.; Radhi, Ahmed Dheyaa; Solke, Nitin; Sekhar, Ravi
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1785

Abstract

Modern power electronics depend heavily on Multilevel Inverters (MLIs) to drive high-power systems operating in renewable energy systems electric vehicles along with industrial motor drives. MLIs create AC signals of high quality by joining multiple DC voltage sources which leads to minimal harmonic distortion outputs. The Cascaded H-Bridge MLI (CHB-MLI) stands out as a first choice among different topologies of MLI for photovoltaic (PV) applications because it includes modular features with fault tolerance capabilities and excellent multi-DC source integration. To achieve effective operation MLIs need optimized control strategies that reduce harmonics while maintaining highest performance. Using SHE-PWM technology provides an effective technique for harmonic frequency reduction which allows the improvement of waveform integrity. Technical restrictions make the solution of SHE-PWM nonlinear equations exceptionally challenging to implement. The resolution of complex non-linear equations requires implementation of GA combined with PSO and BO for optimal switching angle determination. The research investigates an 11-level asymmetric CHB-MLI using five solar panels where SHE-PWM switching angles are optimized through GA, PSO and BO applications. Simulation tests validate that the implemented algorithms succeed in minimizing Total Harmonic Distortion (THD) and removing fundamental harmonic disturbances. The evaluation demonstrates distinct capabilities of each optimization approach between accuracy rates and computational speed performance. These optimization methods yield practical advantages which boost the performance of multi-level inverters. The researchers who follow should study actual hardware deployments together with combined control approaches to enhance power electronic applications.
Optimizing Small-Scale Wind Energy Generation: Site-Specific Wind Speed Analysis and Turbine Placement Strategies Ahmed, Shouket A.; Çiçek, Adem; Bektas, Enes; Yassin, Khalil Farhan; Radhi, Ahmed Dheyaa; Awad, Raad Hamza; Almalaisi, Taha Abdulsalam; Itankar, Nilisha; Sekhar, Ravi; Ahmed, Ahmed H.
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1792

Abstract

Wind is an effective renewable power source suitable for localized electricity production when regional environmental factors have substantial impact on system output. The research studies the best wind turbine placement through wind speed variability studies conducted with calibrated anemometers and data loggers that assess site conditions. A data-based assessment method creates the research's main contribution which facilitates the optimization of wind power potential measurement for enhanced energy efficiency. The research methodology includes continuous Vantage Pro2 equipment together with anemometers at different heights for wind speed observation while performing accuracy-based calibration analysis. The research shows that elevating the turbine from seven meters to ten meters leads to a 12 percent growth in the amount of power produced. The power output of wind energy decreases as wind speed changes because of environmental conditions so proper installation locations become essential. Energy performance increases best when selecting sites which feature reliable and elevated wind speeds. This research provides useful knowledge about enhancing decentralized power generation through wind energy but it cannot be easily scaled up to bigger systems. The study demonstrates that specific site assessments together with practical recommendations will enhance the efficiency of small-scale wind energy systems.
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.