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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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Optimizing Single-Inverter Electric Differential System for Electric Vehicle Propulsion Applications Moumni, Rachad; Laroussi, Kouider; Benlaloui, Idriss; Mahmoud, Mohamed Metwally; Elnaggar, Mohamed F.
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.v4i4.1542

Abstract

The increasing demand for electric vehicles (EVs) is driven by the urgent need for environmentally friendly transportation. This paper addresses the challenge of optimizing EV drivetrain efficiency by proposing a novel single-inverter electronic differential system for distributed EV drivetrains. The research focuses on reducing system cost and complexity while maintaining high performance. The methodology involves a detailed simulation using MATLAB/Simulink to validate the theoretical soundness of the proposed connection method. The results demonstrate that the proposed system achieves a minimum accuracy rate of 97.5%, marking a significant improvement over traditional dual-inverter systems. This approach not only enhances drivetrain efficiency but also contributes to more compact and cost-effective vehicle designs. Additionally, the findings underscore the potential for further refinement and exploration, suggesting that continued advancements in ED systems could lead to even greater performance gains in the future. This research lays the groundwork for future innovations in EV technology, particularly in the areas of cost reduction and system efficiency.
Optimization of a Robust Sigmoid PID Controller for Automatic Voltage Regulation Using the Nonlinear Sine-Cosine Algorithm with Amplifier Feedback Dynamic Weighted (AFDW) System Ahmed, Islam; Suid, Mohd Helmi; Ahmad, Mohd Ashraf; Ahmad, Salmiah; Jusof, Mohd Falfazli Mat; Tumari, Zaidi Mohd
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1930

Abstract

The given paper presents a robust Sigmoid-based Proportional-Integral-Derivative (SPID) controller for Automatic Voltage Regulator (AVR) systems, optimized using the Nonlinear Sine Cosine Algorithm (NSCA) enhanced with the Amplifier Feedback Dynamic Weighted (AFDW) system. Conventional PID controllers are frequently struggling with parameter variations and external interruptions that lead to instability and reduced performances in AVR systems. The proposed SPID controller overcomes these limitations by incorporating nonlinear sigmoid functions, improving the AVR system's robustness and dynamic response. While the AFDW system improves stability and responsiveness by dynamically adjusting the feedback weight, the NSCA balances exploration and exploitation to optimize controller parameters. The primary contribution of the present research is an overview of the NSCA-SPID controller, which offers superior results in voltage regulation compared to traditional PID and other metaheuristic-tuned controllers, enhancement in settling time, elimination of overshoot, and improvement in steady-state error. Additionally, performance index and statistical performances are used to validate the proposed SPID controller. Simulation results demonstrate significant achievements that emphasize the effectiveness of the NSCA-SPID controller toward enhancing the AVR system stability and controller design’s performance under varying load conditions. Finally, the proposed NSCA-SPID controller provides a promising solution for Enhancing the regulation of voltage in power systems, providing Superior and efficient technique for practical applications.
Analyzing the Flow of Injection Molding for Water Filter Handle: Filling, Packing, and Warpage Achor, Zineb; Zahraoui, Yassine; Tayane, Souad; Ennaji, Mohamed; Gaber, Jaafar
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.v4i4.1561

Abstract

Injection molding is a crucial manufacturing technology for producing complex, high-quality parts at scale, making it essential in various industries, including consumer electronics and automotive sectors. However, a lack of understanding about how injection parameters impact common defects like sink marks, short shots, and warpage often limits the widespread adoption of injection molding. This research aims to bridge this gap by providing a comprehensive digital simulation of the injection molding process within a complex mold cavity. Utilizing Moldex3D and the Finite Volume Method (FVM), this study characterizes essential material properties–viscosity, specific heat, density, and thermal conductivity–and examines the effects of gate location and part design on minimizing weld lines and warpage. The FVM involves dividing the computational domain into a finite number of small control volumes. This method is particularly well-suited for handling complex geometries and flow conditions, facilitating detailed and accurate simulations. This study employs Moldex3D, a leading simulation software in the field of injection molding, to demonstrate the use of CAE for design verification and process innovation. Moldex3D’s advanced capabilities make it an ideal tool for simulating injection molding processes, helping improve the quality of parts and contributing to the overall advancement of molding skills in the industry. The simulations revealed optimal gate locations that significantly improved filling patterns, reduced warpage by 50%, and minimized weld lines, thereby enhancing overall part quality. Key contributions of this research include the identification of critical flow characteristics, the reduction of defect-prone regions, and the enhancement of plastic component rigidity. This study provides valuable insights into optimizing injection molding processes, offering a pathway to improved efficiency and part quality in advanced manufacturing.
Automated Water Cooling and Solar Tracking for Efficiency Improvement of PV Systems: A Systematic Review Hamed, Ahmed Hassan; Sharkawy, Abdel-Nasser; Hamdan, I.; Maghrabie, Hussein M.
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.v4i4.1642

Abstract

This article presented previous efforts for overcoming low photovoltaic (PV) solar panel electrical efficiencies resulted from excess heat problem reached in hot climates. Utilizing water cooling, temperature-controlled water cooling and solar tracking solar systems are discussed in this paper. Water is a perfect medium can be used for absorbing excess heat due to its high thermal capacity, availability and low cost. In addition to, utilizing control systems for water cooling systems based on Arduino unit and microcontroller chip which can be interfaced with Bluetooth, WIFI, and Internet of Things (IOT) enhances saving time and effort in large PV solar plants and PV performance. Solar tracking systems, depend on light-dependent resistors (LDRs) which are resistors operated by incident light, or ultraviolet (UV) sensors which are detectors based on incident ultraviolet radiation sensing enhances PV performance. Solar tracking systems enhances PV electrical efficiency compared to fixed PV panels. PV efficiencies of latest studies were presented and compared. Utilizing water cooling systems enhances PV electrical efficiency up to 30%, using an ON-OFF temperature-controlled water-cooling systems increased overall efficiency up to 51.4% and can reduce consumption of water up to 29.28%. In addition to, using two solar tracking systems enhances PV solar panel efficiency up to 65%. The increase in PV installation faces challenges includes millions of solar waste tons that harms environment and human health. However, it can be eliminated utilizing recycling technologies. Artificial intelligence (AI), machine learning techniques would enhance PV performance analyzing and data collection.
Real-Time Experimental Study of Speed Control for PMSM Drive System on OPAL-RT Simulator Using Radial Basis Function Neural Network Hoang, Xuan Hung; Tran, Thanh Hai; Than, Phan Minh; Ngo, Thanh Quyen; Nguyen, Van Sy; Le, Tong Tan Hoa
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2048

Abstract

This paper addresses the problem of improving speed control accuracy and disturbance rejection capability for Permanent Magnet Synchronous Motors (PMSMs), which are widely used in industrial applications requiring high-performance drives. Conventional controllers such as PID often exhibit limited performance under nonlinear and time-varying conditions. The sliding mode control combined with a Radial Basis Function Neural Network (RBFNN) is proposed to enhance robustness and adaptability to overcome these limitations. The main contribution of this study is the integration of an adaptive RBFNN to estimate and compensate for unknown disturbances in real time, ensuring precise and stable motor operation. The theoretical stability of the system is guaranteed based on Lyapunov’s theory. The proposed method is implemented in a MATLAB/Simulink environment and tested on the OPAL-RT OP5707XG real-time hardware platform. The control system includes a speed loop using the RBFNN and a current loop for field-oriented control. The motor is subjected to varying speed commands in three stages to evaluate performance under dynamic conditions. Simulation results show that the RBFNN controller significantly improves speed tracking accuracy, reduces overshoot, and adapts better to sudden changes compared to conventional PID control. Real-time experimental results further confirm the effectiveness of the controller, despite the presence of noise and hardware delays. Current control performance also demonstrates better torque production and phase symmetry under dynamic loading with the RBFNN. A comparative analysis between simulation and experimental data highlights the practical applicability of the proposed approach.
Person and Activity Recognition Based on Joint Motion Features Using Deep Learning with Drone Camera Yunardi, Riky Tri; Sardjono, Tri Arief; Mardiyanto, Ronny
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1949

Abstract

The increasing demand for drone-based surveillance systems has raised significant concerns about advancements in person and activity recognition based on joint motion features within visual monitoring frameworks. This study contributes to developing deep learning models that improve surveillance systems by using RGB video data recorded by drone cameras. In this study, a framework for person and activity recognition based on 120 datasets is proposed, from drone camera-recorded videos of 10 subjects, each performing six movements: walking, running, jogging, boxing, waving, and clapping. Joint motion features, including joint positions and joint angles, were extracted and processed as one-dimensional series data. The 1D-CNN, LeNet, AlexNet, and AlexNet-LSTM architectures were developed and evaluated for classification tasks. Evaluation results show that AlexNet-LSTM outperformed the other models in person recognition, achieving a classification accuracy of 0.8544, a precision of 0.9161, a recall of 0.8575, and an F1-score of 0.8332, while AlexNet delivered superior performance in activity recognition with an accuracy of 0.8571, a precision of 0.8442, a recall of 0.8599, and an F1-score of 0.8463. The relatively small dataset size used likely favors simpler architectures like AlexNet. These findings highlight the effectiveness of joint motion features for person identification and emphasize the suitability of simpler classifier architectures for activity classification when working with small datasets.
Recent Advances in Artificial Intelligence for Dyslexia Detection: A Systematic Review Pamungkas, Yuri; Rangkuti, Rahmah Yasinta; Karim, Abdul; Sangsawang, Thosporn
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2057

Abstract

The prevalence of dyslexia, a common neurodevelopmental learning disorder, poses ongoing challenges for early detection and intervention. With the advancement of artificial intelligence (AI) technologies in the fields of healthcare and education, AI has emerged as a promising tool for supporting dyslexia screening and diagnosis. This systematic review aimed to identify recent developments in AI applications for dyslexia detection, focusing on the methods used, types of algorithms, datasets, and their performance outcomes. A comprehensive literature search was conducted in 2025 across databases including ScienceDirect, IEEE Xplore, and PubMed using a combination of relevant MeSH terms. The article selection process followed the PRISMA guidelines, resulting in the inclusion of 31 eligible studies. Data were extracted on AI approaches, algorithm types, dataset characteristics, and key performance metrics. The results revealed that machine learning (ML) was the most widely applied method (58.06%), followed by multi-method (22.58%), deep learning (16.13%), and large language models (3.23%). Among the ML algorithms, Random Forest and Decision Tree were the most commonly used due to their robustness and performance on structured datasets. In the deep learning category, CNN were the most frequently used models, especially for image-based and sequential input data. The datasets varied widely, including digital cognitive tasks, EEG, MRI, handwriting, and eye-tracking data, with several studies employing multimodal combinations. Ensemble and hybrid models demonstrated superior performance, with some achieving accuracy rates exceeding 98%. This review highlights that AI, particularly ML and multimodal ensemble methods, holds strong potential for improving the accuracy, scalability, and accessibility of dyslexia detection. Future research should prioritize large-scale, multimodal datasets, interpretable models, and adaptive learning systems to enhance real-world implementation.
Improved Trajectory Tracking for Nonholonomic Mobile Robots Via Dynamic Weight Adjustment in Type-2 Fuzzy Model Predictive Control Hedroug, Mohamed Elamine; Bdirina, El Khansa; Guesmi, Kamel; Nail, Bachir; Tibermacine, Imad Eddine; Ma'arif, Alfian
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1926

Abstract

This paper presents an advanced methodology for trajectory control of non-holographic mobile robots. It addresses the challenges of dynamic environments and system uncertainty by proposing a fuzzy model predictive control (FMPC) system that combines Type-2 fuzzy logic (F2MPC) with model predictive control (MPC) to enhance tracking accuracy and adaptability.  A Takagi-Sugeno (T-S) fuzzy model changes the MPC weighting matrices in real-time based on speed and distance errors, while the Type-2 fuzzy system handles uncertainties better than Type-1 systems. Tests using circular and wavy trajectories show that the Type-2 Fuzzy MPC (F2MPC) works better than traditional methods, achieving fewer tracking errors (Integral Squared Error of 0.0011), faster convergence (in 1.2 seconds), and using 65% less energy for movement than conventional MPC. Robustness tests show the controller's stability under disturbances, with minimal deviation and quick recovery. The results highlight the F2MPC's precision, efficiency, and adaptability, making it a promising solution for complex robotic navigation tasks.  The study found that Type-2 fuzzy logic and predictive control improve trajectory tracking, paving the path for real-world applications and computational optimisations.
Indoor Quadcopter Localization Using Fuzzy-Sliding Mode Control for Robust Navigation Darwito, Purwadi Agus; Agustina, Nilla Perdana; Pratama, Detak Yan; Al Farros, Mohammad Naufal; Setiadi, Iwan Cony; Biyanto, Totok Ruki; Imron, Choirul
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1941

Abstract

Growing demand for warehouse automation requires Unmanned Aerial Vehicles (UAVs), particularly quadcopters, to operate autonomously with a high level of precision and reliability. However, indoor localization poses unique challenges due to the absence of Global Positioning System (GPS) signals, making alternative sensors and robust control strategies essential. This study proposes an indoor UAV navigation system that integrates camera and LiDAR sensors with Fuzzy–Sliding Mode Control (Fuzzy-SMC) to enhance stability and reduce the chattering effects commonly associated with Sliding Mode Control. In the proposed method, the camera provides better accuracy for real-time position tracking compared to LiDAR, while fuzzy logic adaptively adjusts the Sliding Mode Control parameters, which serve as the main controller for stabilizing the quadcopter’s nonlinear dynamics. Research methodology includes mathematical modeling of the UAV quadcopter, the design of the Fuzzy-SMC controller, and simulation-based testing for trajectory tracking in indoor environments. Results show that the developed system achieves high accuracy, with error values ranging from 0 to 4.044%, remaining below the acceptable threshold of 5%. These findings demonstrate that integration of a camera with Fuzzy-SMC provides an effective and reliable solution for indoor quadcopter UAV navigation, while future research will focus on optimizing the fuzzy rule base and conducting hardware validation in real warehouse scenarios.
Chess Optimizer for Load Frequency Control of Three-Area Multi-Source Renewable Energy Based on PID Plus Second Order Derivative Controller Areeyat, Chatmongkol; Audomsi, Sitthisak; Obma, Jagraphon; Yang, Xiaoqing; Sa-Ngiamvibool, Worawat
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2052

Abstract

Renewable energy sources such as solar and wind are increasingly integrated into multi-area power systems. However, their fluctuating and unpredictable characteristics pose challenges for sustaining system stability. Therefore, automatic generation control (AGC) is essential for the continual regulation of power and frequency in the system. This article presents the use of a Proportional–Integral–Derivative plus second-order derivative (PID+DD) controller for load frequency control in a three-area multi-source power system, which includes a thermal reheat power plant with a generation rate constraint (GRC) representing the maximum permissible change rate of generation output of 5% per min , a hydroelectric power plant with a  GRC of 370% per min, and a wind power plant where wind speeds vary across areas. The power generation ratio of the three areas is 1:2:4. The controller parameters were tuned using a Chess Optimizer (CO), a metaheuristic inspired by chess move complexity and planning, with specific weights assigned to each type of chess piece. Two load change scenarios were studied: a 10% step load perturbation (10% SLP) and a random load pattern (RLP).  Furthermore, experimental results based on the Integral of Time-weighted Absolute Error (ITAE) indicate that the PID+DD controller tuned by the Chess Optimizer achieved the lowest steady-state error in both scenarios (10% SLP and RLP). In Case 1 (SLP), it achieved an ITAE of 25.5072, representing a 9.70% reduction compared to the PID controller and a 1.96% reduction compared to the PI controller. In Case 2 (RLP), it achieved an ITAE of 88.0654, representing a 1.14% reduction compared to the PID controller and a 2.03% reduction compared to the PI controller. These improvements contribute to enhanced oscillation damping, reduced overshoot and undershoot, and improved frequency stability, demonstrating the practical applicability of the proposed approach in future smart grids with high renewable energy penetration.

Page 36 of 37 | Total Record : 361

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