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Alfian Ma'arif
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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 35 Documents
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Search results for , issue "Vol 5, No 1 (2025)" : 35 Documents clear
Theoretical and Experimental Investigation of the Effect of Linear Fluid Power Control System Design on its Static and Dynamic Performance Qassim, A. I.; Sadak, Tahany W.; Rizk, Mahassen
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1550

Abstract

Fluid power control systems are widely used in automated systems like manufacturing, biomedical treatments, and food handling, as well as in optimizing aircraft wing design, mobile applications, and thermal management in electronic devices, energy transformation, and aerospace applications. This study investigated the static and dynamic characteristics of a linear fluid power control system utilizing either a servo control valve (SV) or a proportional directional flow control valve (PV). The study focused on evaluating performance differences between these two valve types while maintaining a constant oil temperature at 30°C. Experimental tests were conducted under varying supply pressures, loads, and valve types. A system was built to conduct real-time experiments. In this paper we studied the effect of valve flow rate at full opening, the actual supply pressure-decay, and studied the effect of the loading system on the performance. The aim of this paper is to find out which control valve is better in static and dynamic performance in real-world. Through comparing two hydraulic control valves designs, the experiment results show that the servo control valve (SV) offers a clear advantage over the proportional directional flow control valve (PV) in linear fluid power control systems operating at a constant temperature. The SV designs demonstrated superior performance in terms of flow rate, pressure retention, and dynamic response. This makes SV an optimal choice for applications requiring high flow rates, consistent pressure, and precise, rapid adjustments, especially in high-speed operations.
Self-Motion Control Exoskeleton for Upper Limb Rehabilitation with Perceptron Neuron Motion Capture Osman, Mohamad Afwan; Azlan, Norsinnira Zainul; Suwarno, Iswanto; Samewoi, Abdul Rahman; Kamarudzaman, Nohaslinda
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1030

Abstract

Upper limb rehabilitation robot can facilitate patients to regain their original impaired arm function and reduce therapist’ workload. However, the patient does not have a direct control over his/ her arm movement, which may lead to discomfort or even injury. This paper focuses on the development of a self-motion rehabilitation robot using Perception Neuron motion capture, where the movement of the impaired arm imitates the motion of the healthy limb.  The Axis Neuron software receives the healthy upper limb’s motion data from Perception Neuron. Unity serves as the simulation engine software that provides a 3-dimensional animation. ARDUnity acts as the communication platform between Unity software with Arduino. Arduino code is generated using Wire Editor, which avoids the need of the programming to be written in C++ or C#. Finally, Arduino instructs the exoskeleton motors that are connected to the impaired arm to move, following the healthy joint’s motion. The forward kinematics analysis for the robotic exoskeleton has been carried out to identify its workspace. Hardware experimental tests on the elbow and wrist flexion/ extension have shown the root-mean-square errors (RMSE) between the healthy and impaired arms movement to be 1.5809○ and 12.1955○ respectively. The average time delay between the healthy and impaired elbow movement is 0.1 seconds. For the wrist motion, the time delay is 1 second. The experimental results verified the feasibility and effectiveness of the Perception Neuron in realizing the self-motion control robot for upper limb rehabilitation. The proposed system enables the patients to conduct the rehabilitation therapy in a safer and more comfortable way as they can directly adjust the speed or stop the movement of the affected limb whenever they feel pain or discomfort.
Optimized Vector Control Using Swarm Bipolar Algorithm for Five-Level PWM Inverter-Fed Three-Phase Induction Motor Yaseen, Farazdaq R.; Al-Khazraji, Huthaifa
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1713

Abstract

Induction motors (IMs) are commonly used in various applications such as robotics and automotive industries. This paper proposes an optimization of two proportional-integral (PI) controllers for a multi-level pulse width modulation (PWM) voltage-fed inverter linked to a three-phase IM. The paper aims to enhance inverter output quality, minimize harmonic distortion, and ensure robust, stable performance. The swarm bipolar algorithm (SBA) is introduced to elaborate the searching of the best settings of the PI controllers to achieve the desired response.  Harmonics lead to increased system losses by creating negative torque components. To address this problem, two modulation algorithms are proposed to generate three-phase voltage with minimum harmonics including space vector PWM (SVPWM) inverter and sinusoidal PWM (SPWM). Simulation results based on MATLAB/Simulink environment for various operation conditions such as sudden loads change and speed changes reveal that the proposed controller enhances the system's performance. Moreover, the five-level SVPWM inverter has a minimum threshold harmonic distortion (THD) compared to the five-level SPWM inverter where the THD is decreased from 40.24% for SPWM method to 13.67% for the SVPWM method.
A Review of Advanced Force Torque Control Strategies for Precise Nut-to-Bolt Mating in Robotic Assembly Ting, Terence Sy Horng; Goh, Yeh Huann; Chin, Kar Mun; Tan, Yan Kai; Chiew, Tsung Heng; Ma, Ge; How, Chong Keat
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1604

Abstract

Achieving precise alignment in high-precision robotic assembly is critical, where even minor misalignments can cause significant issues. Various control strategies have been developed to tackle these challenges, including passive compliance control (PCC), active control (AC), and manual teaching method (MTC). While AC is valued for its real-time adaptability, PCC and MTC offer advantages in simpler, cost-effective applications.   This review evaluates these strategies, emphasizing the integration of AI and machine learning to address the limitations of traditional AC methods, such as spiral and tilt searches, which are rigid, slow, and computationally demanding, making them unsuitable for dynamic environments. Machine Learning (ML) and Artificial Intelligence (AI) offer data-driven improvements in performance and adaptability over time. Techniques like Linear Regression, Artificial Neural Networks (ANNs), and Reinforcement Learning (RL) are explored for enhancing precision and real-time adaptability in complex tasks. These AI methods are applied in real-world industries, such as automotive and electronics manufacturing. The review compares control strategies and AI techniques, analyzing trade-offs in accuracy, speed, computational efficiency, and cost. It also discusses future directions, including hybrid control systems, advanced sensor integration, and more sophisticated AI algorithms. Ethical and safety considerations are highlighted, particularly in industrial settings where reliability and human-robot interaction are critical. This comprehensive review demonstrates AI's potential to enhance precision, reduce manual intervention, and improve performance in high-precision robotic assembly while guiding the selection of appropriate methods for specific applications.
Comparative Analysis of 1D – CNN, GRU, and LSTM for Classifying Step Duration in Elderly and Adolescents Using Computer Vision Lee, Teng Hong; Shair, Ezreen Farina; Abdullah, Abdul Rahim; Rahman, Kazi Ashikur; Ali, Nursabillilah Mohd; Saharuddin, Nur Zawani; Nazmi, Nurhazimah
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1588

Abstract

Developing a classification system that can predict the onset of neurodegenerative diseases or gait-related disorders in elders is vital for preventing incidents like falls. Early detection allows reduction in symptoms and treatment cost for the elderly. In this study, step duration data from five healthy adolescents with age range of 23 – 29 years old and five healthy elderly individuals with age range of 71 – 77 years old were sourced from PhysioNet. To ensure proper training of the deep learning models, synthetic data was generated from the original dataset using a noise jittering technique with random noise of a range between -0.01 and 0.01 added to the original data. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and 1D Convolutional Neural Network (1D-CNN) are used for training the data since the data is available in the form time series data. LSTM and GRU are advanced forms of Recurrent Neural Network (RNN) while 1D – CNN can capture temporal dependencies in sequential data. 1D – CNN has the advantages over GRU and LSTM of being more robust to noise and can capture complex patterns behind the data. These methods will be compared in terms of processing time and accuracy. Results show that 1D – CNN outperforms both LSTM and GRU with accuracy of 1.000 in less than 60 seconds. The novelty and contribution of this research shows that healthy old people and healthy young people can be classified with deep learning. Further direction of the research can explore the deep learning in classification of Parkinson’s disease.
Comparison of Proportional Integral Derivative and Fuzzy Logic Controllers: A Literature Review on the Best Method for Controlling Direct Current Motor Speed Putra, Agus Mulya; Maradona, Hendri; Rohmah, Rina Ari
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1701

Abstract

Control systems, particularly for DC motors, are a continually evolving field with various methods and techniques aimed at improving control system performance. Common issues in DC motor control, such as high overshoot and inadequate response times, highlight the need for further research into more effective tuning techniques. This study compares conventional PID and FLC methods in controlling DC motor speed, while also exploring optimization potential through new approaches like hybrid methods and the use of neural networks. The contributions of this research include a comprehensive analysis of previous studies on DC motor control performance and an in-depth assessment of the effectiveness of PID and FLC methods in addressing rise time, settling time, and overshoot issues. The methodology used in this study is a literature review, which involves collecting and analyzing various studies related to the application of both methods in DC motor control. Literature selection criteria include relevance, methodology used, and contributions to scientific advancements in motor control. The analysis shows that FLC performs better in handling overshoot, with previous studies indicating its ability to completely eliminate overshoot. Although the PID method is simpler and easier to apply in systems with linear characteristics, FLC offers better flexibility and adaptability for managing uncertainty and non-linear systems. Recommendations for further research are also presented, including a deeper exploration of integrating the two methods in a hybrid control system to enhance motor control performance.
UAV Logistics Pattern Language for Rural Areas Rahmananta, Radyan; Airlangga, Gregorius; Sukwadi, Ronald; Basuki, Widodo Widjaja; Sugianto, Lai Ferry; Nugroho, Oskar Ika Adi; Kristian, Yoel
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1554

Abstract

The logistical challenges in rural areas, which often face limited infrastructure, varied terrains, and dispersed populations, often lead to inefficient and costly delivery systems. Recent developments in Unmanned Aerial Vehicle (UAV) technology offer a theoretical framework for overcoming these challenges. This research proposes a comprehensive pattern language specifically designed for multi-UAV logistics operations in rural settings. The proposed system integrates critical components such as LiDAR-based map generation, altitude information storage, partial goal estimation, and collision avoidance into a unified framework. Unlike existing research that typically focuses on isolated aspects like route optimization or payload management, this system features an advanced path planning algorithm capable of real-time environmental assessment and direction-aware navigation. Focus group discussions with logistics experts from Talaud Island, North Sulawesi, Indonesia informed the design and refinement of the proposed patterns, ensuring that they address the practical needs of rural logistics. Our analysis suggests that this system offers a theoretical foundation for significantly improving the efficiency, reliability, and sustainability of delivering essential goods and services to rural areas, thereby supporting equitable development and improving the quality of life in these communities. While no empirical data is presented, the framework serves as a scalable foundation for future implementations of UAV-based rural logistics systems.
Optimal Controller Design of Crowbar System Using Class Topper Optimization: Towards Alleviating Wind-Driven DFIGs Under Nonstandard Voltages Elnaggar, Mohamed F.
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1694

Abstract

Increased integration of doubly fed induction wind generators (DFIWG), power sector deregulation, rising energy demands, and technological breakthroughs are all contributing to the rapid advancement of modern energy infrastructure. These advancements, nevertheless, pose serious challenges to maintaining fault ride-through capability (FRTC) in DFIWG. Thus, this work proposes a novel FRTC enhancement method that uses a crowbar system with a class topper optimization (CTO) based control technique. The crowbar system and DFIWG are integrated with the investigated system to achieve FRTC, reduce injected harmonic distortion, and maintain the DC link voltage (DCLV) below the permitted level. Additionally, the system has a DCLV control system that uses a CTO-PI controller to maintain an enclosure DCLV, which enhances crowbar performance. The findings demonstrated that when a CTO-based controller is employed, the DFIWG system reacts slightly better to angular speed, active and reactive power, DCLV, and generator speed. The MATLAB/Simulink scenarios used to test the suggested system show that it can achieve FRTC and allow for a high penetration potential of DFIWG.
Hybrid PI-MPC Control System for a Four-Phase Interleaved Boost Converter: Performance Evaluation in Reducing Current Ripple in Electric Car Battery Charging Ikawanty, Beauty Anggraheny; Safitri, Hari Kurnia; Fauziyah, Mila; Irawan, Bambang; Taufik, Taufik; Risdhayanti, Anindya Dwi
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1677

Abstract

Electric car batteries face two primary challenges: the substantial number of batteries used, leading to increased weight and costs, and the limited battery lifespan, which results in high maintenance expenses. To address these issues, a power supply with high voltage gain and optimal efficiency is essential. Currently, switching mode power supplies are preferred due to their superior efficiency over linear systems. Among these, DC-DC boost converters are key components. However, conventional boost converters face limitations such as restricted voltage gain and significant current ripple, which negatively affect battery performance and system efficiency. This study aims to design a hybrid control system for a four-phase interleaved boost converter, integrating Model Predictive Control (MPC) with Proportional-Integral (PI) control. The hybrid control system dynamically adjusts the PI controller's setpoint based on real-time input variations, enhancing the system’s responsiveness and stability under fluctuating load and voltage conditions. The experimental setup includes a four-phase interleaved boost converter with split inductance and capacitance bypass techniques to mitigate ripple effects. Our hypothesis posits that the hybrid PI-MPC control system will reduce current ripple and improve system performance in electric vehicle battery applications. Results show a significant reduction in input current ripple (0.0014%) and output current ripple (0.042%), indicating improved performance compared to conventional converters. Despite these improvements, the study acknowledges limitations related to the scalability of the proposed system and potential challenges in integrating this topology into larger systems. Further investigation is required to assess its long-term performance and economic feasibility in diverse EV applications.
Efficient Vision-Guided Robotic System for Fastening Assembly Using YOLOv8 and Ellipse Detection in Industrial Settings Tan, Yan Kai; Chin, Kar Mun; Goh, Yeh Huann; Chiew, Tsung Heng; Ting, Terence Sy Horng; MA, Ge; How, Chong Keat
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1705

Abstract

The assembly of fastening components traditionally relies on labour-intensive human-machine collaboration, which incurs high costs. Existing methods often assume fixed positions or use markers for guidance, requiring extra effort to place and maintain them. This study aims to develop an intelligent control system for a vision-equipped robotic arm to autonomously assemble fastening components in industrial settings, enhancing flexibility and reducing labour costs. The system integrates object detection with edge and ellipse detection, alongside filtering techniques, to accurately locate the centres of the fastening components.  The key contribution is the system's ability to perform autonomous assembly without predefined positions, enhancing flexibility in varied environments. YOLOv8 is employed to detect the bolt and nut, followed by edge and ellipse detection to pinpoint centre coordinates. A depth camera and kinematic calculations enable accurate 3D positioning for pick-and-place tasks. Experimental results demonstrate the system’s high effectiveness, with less than 1% of targets undetected. Based on experiments conducted in randomly arranged conditions, the system demonstrated high effectiveness, achieving over 99% detection accuracy. It achieved an 87% average success rate for picking fastening components ranging from sizes M6 to M18, and a 90% success rate for precise placement. Additionally, the system demonstrated robustness across various component sizes, with a minor increase in orientation errors for smaller components, attributed to depth estimation challenges. Future work could explore alternative depth data collection methods to improve accuracy. These results confirm the reliability of the system in flexible assembly tasks, demonstrating its potential to reduce costs by minimising manual involvement in industrial settings.

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