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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 361 Documents
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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.
Altitude Controller Based on Artificial Neural Network Genetic Algorithm for a Quadcopter MAV Ibarra, José Ramón Meza; Ulloa, Joaquın Martınez; Pacheco, Luis Alfonso Moreno; Cortes, Hugo Rodrıguez
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1582

Abstract

Mechanical systems with high dynamic complexity often face challenges due to unmodeled uncertainties and external perturbations, making effective control difficult. Therefore, new advanced, robust, intelligent control theories have been developed through the sudden advance of computational power in recent years. In this research work, these new theories of automatic control are used, mainly based on what is currently called Artificial Intelligence (AI) algorithms, to develop a novel altitude controller based on the theory of Genetic Algorithms (GA) and Artificial Neural Networks (ANN).Theperformance of the designed controller is evaluated by employing the numerical simulation model in MATLAB SIMULINK, which was created for the commercial MAV Mambo Parrot. The developed intelligent ANN-GA controller uses the Levenberg-Marquardt optimization method and a Genetic Algorithm (GA) to improve Artificial Neural Network performance. The initial PID gains are obtained according to the GA, generating optimal values that initialize the neural network and contribute to optimal performance of the ANN training through evaluation of (Mean Square Error) MSE and (Integral Time Absolute Error) ITAE; the ANN takes then, the adequate output and signals as data from input to calculate the required combination of gains as output for MAV altitude controller. Simulation results demonstrate that the self-tunable controller improves the settling time, decreasing by 31.6% compared to the original PID controller. The certainty of the implemented controller opens new routes for automatic control strategies based on artificial intelligence algorithms for the complex nonlinear dynamics of unmanned aircraft.
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.
Application of Artificial Neural Networks in Predicting Internal Combustion Engine Performance and Emission Characteristics: A Review of Key Methodologies and Findings Mohasab, Hamada; Abouelsoud, Mostafa; Shmroukh, Ahmed N.; Ghazaly, Nouby
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1584

Abstract

The global need for fuel-efficient coupled with minimizing the environmental impacts of ICEs. This review paper highlights how different ANN methodologies such as backpropagation, recurrent neural networks (RNNs), and long short-term memory (LSTM) networks have been applied to optimize engine calibration, improve fuel efficiency, and minimize emissions across a wide range of fuel blends, including hydrogen-gasoline and ethanol-gasoline mixtures. The research focuses on the application of ANN models to predict performance indicators such as brake thermal efficiency, brake-specific fuel consumption, and emissions, reducing reliance on costly and time-consuming experimental tests. The methodology involved a systematic review of peer-reviewed studies published between 2010 and 2024. Studies were selected based on criteria such as relevance to ICE performance and emission control, use of ANN methodologies, and the availability of experimental or simulation data for validation. involves the use of advanced ANN architectures, including backpropagation, RNNs, and LSTM networks, to establish nonlinear relationships between input parameters such as engine speed, load, and fuel type, and output performance indicators. Findings show that comparison between real model and developed program enhanced from ANN model make a difference prediction capability for engine performance enhanced by at least 10 to 15 % of the traditional modeling. techniques, provide better calibration method of ICEs for better fuel consumption. efficiency and reduced emissions. This present study seeks to establish itself in matters that have not been explored in other papers or researches as follows. integration of Hybrid ANN models, which are better than conventional methods in two major trends, one of which is the improvement of the predictive accuracy and the other is the achievement of increased computational efficiency. It is found that the ANN methodologies presents a strong armory in improving the performance of ICE coupled with lowering of emissions with the possibilities of additions for further enhancements of the technology through the incorporation of other machines use of learning techniques in the future studies.
Sensorless Speed Estimation Basing on MRAS Model for a PMSM Machine Application Elnaggar, Mohamed F.; Aymen, Flah; Mourad, Dina
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1585

Abstract

Wind energy systems utilizing synchronous machines can encounter challenges with speed detection at high rotational speeds due to increasing motor temperatures affecting parameters like stator resistance. This paper addresses these challenges by proposing a novel high-speed estimator algorithm based on the Model Reference Adaptive System (MRAS) approach. The primary contribution of this research is the development of an MRAS-based speed estimator that leverages a reactive power model to maintain robustness against variations in stator resistance, even at elevated speeds. To optimize the estimator’s performance, we employed a particle optimization algorithm for tuning, which overcomes issues related to regulator parameter identification. We implemented the proposed algorithm in Matlab and validated it on a real machine prototype capable of high-speed operation. After a comparison wth 5 different methods, the results indicate that the estimator performs effectively up to 42,000 RPM (600 Hz), demonstrating a maximum speed estimation error of 50 Hz. Stability analyses across various speed regions and practical lab tests confirm the robustness and accuracy of the proposed control scheme. The findings highlight the estimator’s improved performance in high-speed scenarios, showcasing its potential for enhancing speed detection in wind energy 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.
Adaptive Fuzzy Logic Control of Quadrotor Yasmine, Zamoum; Karim, Baiche; Razika, Boushaki; Younes, Benrabah
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1583

Abstract

Intelligent controllers are created in this work to regulate the attitude of quadrotor UAVs (Unmanned Aerial Vehicles). Quadrotors offer a wide range of real-time applications, including surveillance, inspection, search and rescue, and lowering the human force safety risks. The kinematics of quadrotor are similar to those of an inverted pendulum. To maintain balance, they must continuously adjust orientation and thrust. External disturbances, like wind or sudden movements, can easily destabilize them, necessitating sophisticated control algorithms for stable flight and precise maneuverability. This instability poses a significant challenge in designing and operating quadrotors, especially in dynamic environments where real-time adjustments are crucial for maintaining control. To avoid any form of damage, a mathematical model should be constructed first, followed by the implementation of various control systems. A thorough simulation model for a Quadrotor is presented in this project. The quadrotor is a six degrees of freedom object, it has six variables to express its position in space where (x, y and z) represent the distance of quadrotor from an earth fixed inertial form to its center of mass, main movements of roll, pitch, yaw are the Euler angles representing the orientation of the quadrotor at each axis. The proposed control techniques are applied separately: PID Controller, Fuzzy Logic PID Controller and Adaptive Fuzzy Logic PID Controller. The purpose of this work is to asses these control techniques for the motions of a Quadrotor in terms of better performance, tracking error reduction, and stability. MATLAB software is used for modeling, control, and simulation. According to the obtained results, the PID controller provided the best settling time. In addition, when we applied fuzzy logic PID control to adjust the pitch angle, the system experienced overshoot; however, with Adaptive Fuzzy Logic PID controller, the system provided the best performance according to the desired criteria.
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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