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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 361 Documents
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The Utilization of a TSR-MPPT-Based Backstepping Controller and Speed Estimator Across Varying Intensities of Wind Speed Turbulence Elzein, I. M.; Maamar, Yahiaoui; Mahmoud, Mohamed Metwally; Mosaad, Mohamed I.; Shaaban, Salma Abdelaal
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.v5i2.1793

Abstract

Because wind systems are so prevalent in the electrical grid, an innovative control method can significantly increase the productivity of permanent magnet synchronous generators (PMSG). A wind power generation system's maximal power point (MPP) tracking control approach is presented in this paper. The nonlinear backstepping controller, which is robust to parameter uncertainty, is used in this work to enhance the tip speed ratio approach.  To lower the system's equipment and maintenance costs, we suggested utilizing a speed estimator. As a novel addition to the backstepping controller development, the suggested estimator is a component of the backstepping controller development. The control and system organization approaches are presented. Lyapunov analysis is used to guarantee the stability of the controller. To assess the suggested approach, step change and varying wind speed turbulence intensities are employed. The results expose the great efficiency of the proposed method in both tracking MPP and calculating generator speed.  The proposed control strategy and structure are validated by MATLAB simulations.
Collision Avoidance in Mini Autonomous Electric Vehicles Using Artificial Potential Fields for Outdoor Environment Saputro, Joko Slamet; Juliatama, Hanif Wisti; Adriyanto, Feri; Maghfiroh, Hari; Apriaskar, Esa
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.v5i2.1708

Abstract

The rapid advancement of technology is driving the transition toward Society 5.0, where intelligent transportation systems enhance safety, efficiency, and sustainability. One of the biggest challenges in transportation is the high frequency of vehicle accidents, with approximately 80% attributed to driver error. To mitigate this, Advanced Driver Assistance Systems (ADAS) have been developed to improve vehicle autonomy and reduce accidents. This research proposes a potential field-based collision avoidance system for autonomous vehicle navigation, where the vehicle and obstacles act as positive poles, repelling each other, while the target destination serves as a negative pole, attracting the vehicle. Experimental results demonstrate a GPS positioning error of 1.55 m with a 66% success rate and LiDAR sensor accuracy of 96.4%, exceeding the required 95% threshold. Obstacle avoidance was tested with two safety thresholds (2 m and 2.5 m) across single- and two-obstacle scenarios. The 2 m threshold resulted in shorter travel distances (16.406 m vs. 16.535 m for 2.5 m) and faster completion times (19.036 s vs. 19.144 s), while the 2.5 m threshold provided greater clearance. GPS accuracy was significantly influenced by HDOP values and satellite count, with lower HDOP improving trajectory precision. The system successfully adjusted its trajectory in response to obstacles, ensuring effective real-time navigation.
A YOLO-Based Target Detection Algorithm for DJI Tello Drone Baharuddin, A'dilah; Basri, Mohd Ariffanan 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.v5i3.1898

Abstract

The expansion of the application of drones has dispersed in wide range across military and civilian sectors. The application in such search and rescue missions are applicable with integration of computer vision and machine learning. A key feature of the drone for such applications is the capability to detect and locate objects and targets. Despite traditional methods perform excellently, deep-learning methods are the game changer in detection due to their better accuracy and robustness, rendering them ideal for real-time applications. The methods, including the YOLO series, are in continuous development to further enhance their performance. however, the regular issuance of updated and newer versions has intrigued curiosity regarding the potential superiority of the newer version over the previous versions in drone application. Hence, this paper has chosen the YOLOv8, YOLOv5u and YOLOv11 models for implementation on a DJI Tello drone to detect a custom target. A dataset for the target as a single class to be trained and validated is generated through images annotation. The target is required to be captured in the position of middle of the frame. However, the analysis upon performance metrics found that every model recorded high rates of precision, accuracy and recall. Yet, the simulations and experimentations displayed the ability of the model to accurately recognize the target. Thus, in order to evaluate the performance of each model thoroughly, it is advisable to ensure the data is sufficient and unbiased, while properly choosing the right setting parameters to the YOLO models.
Utilizing Short-Time Fourier Transform for the Diagnosis of Rotor Bar Faults in Induction Motors Under Direct Torque Control Bousseksou, Radouane; Bessous, Noureddine; Elzein, I. M.; Mahmoud, Mohamed Metwally; Ma'arif, Alfian; Touti, Ezzeddine; Al-Quraan, Ayman; Anwer, Noha
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.v5i2.1886

Abstract

Industrial applications rely heavily on induction motors (IMs). Even though any IM problem can seriously impair operation, rotor bar failures (RBFs) are among the toughest to identify because of their detection challenges. RBFs in IMs can significantly impact performance, leading to reduced efficiency, increased vibrations, and potential IM failure. This research provides a thorough analysis of diagnosing these issues by detecting RBFs and evaluating their severity using three sophisticated signal processing techniques (Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), and Discrete Wavelet Transform (DWT)). The three techniques (FFT, DWT, and STFT) are used in this work to assess the stator currents. An accurate mathematical model of the IM under RBFs serves as the basis for the simulation. The robustness of Direct Torque Control (DTC) is assessed by examining the IM's behavior in both normal and malfunctioning situations. Although the results show that DTC successfully preserves motor stability even when there are flaws, the current analysis offers some significant variation. The findings show that when it comes to identifying RBFs in IMs and determining their severity, the STFT performs better than FFT and DWT. The suggested method maintains low estimation errors and strong performance under various operating situations while providing high failure detection accuracy and the ability to discriminate between RBFs.
Trapezoidal Scheme for the Numerical Solution of Fractional Initial Value Problems Batiha, Iqbal M.; Alsamad, Hebah F.; Jebril, Iqbal H.; Al-Khawaldeh, Hamzah O.; Kasasbeh, Wala’a A. Al; Momani, Shaher
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.v5i2.1795

Abstract

The purpose of this study is to recall the main concepts and definitions in relation to the fractional calculus. In light of this overview, we will propose a novel fractional version of the so-called Trapezoid method named by the fractional Trapezoid method. Such a method will then be used to numerically solve the analog version of the initial value problems called fractional initial value problem FIVPs. As consequences of the proposed numerical approach, several numerical examples will be illustrated to verify the efficiency of the proposed numerical approach.
Hierarchical Cascaded Takagi-Sugeno Model Predictive Control for Performance Enhancement of Doubly-fed Induction Generator-Based Wind Turbine Systems Aggoune, Amira; Berrezzek, Farid; Khelil, Khaled
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.v5i2.1786

Abstract

This paper proposes a cascaded Takagi-Sugeno Model Predictive Controller (TS-MPC) for a Doubly-fed Induction Generator (DFIG) based Wind Power Conversion System (WPCS) to maximize power extraction, maintain zero stator reactive power, and enhance power quality. For this purpose, the Takagi-Sugeno Fuzzy Logic Control (TS-FLC) is arranged in a sequential configuration with the Finite Control-Set Model Predictive Control (FCS-MPC) strategy to enhance the overall performance of the wind power system. The introduced control technique, which is applied to govern the Rotor Side Converter (RSC) of the DFIG, consists of two cascaded control loops for achieving Maximum Power Point Tracking (MPPT). The innermost control loop is implemented to regulate the d-q axis rotor currents using FCS-MPC strategy. Meanwhile the outermost control loop is employed to regulate the DFIG’s rotational speed pursuant to the Tip Speed Ratio MPPT (TSR-MPPT) control framework using the TS-FLC, thus improving the predictive accuracy and control effectiveness.  To validate the performance of the devised control scheme, a numerical simulation of a 1.5MW DFIG based WPCS was conducted using MATLAB/Simulink software. The simulation results demonstrate that the proposed cascaded TS-MPC not only outperforms the cascaded PI-MPC in terms of superior adaptability to nonlinearities and varying wind conditions—thanks to the inherent flexibility of TS-FLC—but also in various performance metrics, including response time, steady-state error, and total harmonic distortion (THD).Furthermore, while FCS-MPC approaches are often criticized for computational complexity, the TS-FLC structure enhances real-time feasibility by reducing computational overhead compared to conventional FLC methods. These findings reinforce the practical viability of TS-MPC for large-scale wind energy applications and indicate the effectiveness of the proposed control scheme.
Deep Learning Approach to Lung Cancer Detection Using the Hybrid VGG-GAN Architecture Pamungkas, Yuri; Kuswanto, Djoko; Syaifudin, Achmad; Triandini, Evi; Hapsari, Dian Puspita; Nakkliang, Kanittha; Uda, Muhammad Nur Afnan; Hashim, Uda
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.v5i3.1923

Abstract

Lung cancer ranks among the primary contributors to cancer-related deaths globally, highlighting the need for accurate and efficient detection methods to enable early diagnosis. However, deep learning models such as VGG16 and VGG19, commonly used for CT scan image classification, often face challenges related to class imbalance, resulting in classification bias and reduced sensitivity to minority classes. This study contributes by proposing an integration of the VGG architecture and Generative Adversarial Networks (GANs) to improve lung cancer classification performance through balanced and realistic synthetic data augmentation. The proposed approach was evaluated using two datasets: the IQ-OTH/NCCD Dataset, which classifies patients into Benign, Malignant, and Normal categories based on clinical condition, and the Lung Cancer CT Scan Dataset, annotated with histopathological labels: Adenocarcinoma, Squamous Cell Carcinoma, Large Cell Carcinoma, and Normal. The method involves initial training of the VGG model without augmentation, followed by GAN-based data generation to balance class distribution. The experimental results show that, prior to augmentation, the models achieved relatively high overall accuracy, but with poor performance on minority classes (marked by low precision and F1-scores and FPR exceeding 8% in certain cases). After augmentation with GAN, all performance metrics improved dramatically and consistently across all classes, achieving near-perfect precision, TPR, F1-score, and overall accuracy of 99.99%, and FPR sharply reduced to around 0.001%. In conclusion, the integration of GAN and VGG proved effective in overcoming data imbalance and enhancing model generalization, making it a promising solution for AI-based lung cancer diagnostic systems.
Stability Analysis of a Fractional-Order Lengyel–Epstein Chemical Reaction Model Bouaziz, Khelifa; Djeddi, Nadhir; Ogilat, Osama; Batiha, Iqbal M.; Anakira, Nidal; Sasa, Tala
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.v5i2.1848

Abstract

In this paper, we stady a mathematical model based on a system of fractional-order differential equations to describe the dynamics of the Lengyel–Epstein chemical reaction, which is well known for exhibiting oscillatory behavior. The use of fractional derivatives allows in chemical processes compared to classical integer-order models. We specifically focus on analyzing the stability of the system’s positive equilibrium point by applying fractional calculus techniques. The stability conditions are derived and discussed in the context of the fractional-order parameters. To validate the theoretical findings, we perform numerical simulations using the Forward Euler method adapted for fractional-order systems. These simulations illustrate the impact of the fractional order on the system’s dynamic behavior and confirm the analytical results regarding equilibrium stability.
ESI-YOLO: Enhancing YOLOv8 with Efficient Multi-Scale Attention and Wise-IoU for X-Ray Security Inspection Haq, Arinal; Suciati, Nanik; Bui, Ngoc Dung
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.v5i3.1983

Abstract

Security inspection is a priority for preventing threats and criminal activities in public places. X-ray imaging can help with the closed luggages checking process. However, interpreting X-ray images is challenging due to the complexity and diversity of prohibited items. This paper proposes ESI-YOLO, an enhanced YOLOv8-based model for prohibited item detection in X-ray security inspection. The model integrates Efficient Multi-Scale Attention (EMA) and Wise-IoU (WIoU) loss function to improve multi-scale feature representation and detection accuracy. EMA improves multi-scale feature representation, while WIoU enhances bounding box regression, particularly in cluttered and overlapping scenarios. Comprehensive experiments on the CLCXray and PIDray datasets validate the effectiveness of ESI-YOLO. A systematic exploration for the optimal placement of EMA integration on YOLOv8 architecture reveals that the scenario with direct integration in both backbone and neck sections emerges as the most effective configuration without introducing significant computational complexity. Ablation experiments demonstrate the synergistic effect of combining EMA and WIoU in ESI-YOLO, outperforming individual component additions. ESI-YOLO demonstrates notable advancements over the baseline YOLOv8 model, achieving mAP50 improvements of 0.9% on CLCXray and 3.5% on the challenging hidden subset of PIDray, with a computational cost of 8.4 GFLOPs. Compared to other nano-sized models, ESI-YOLO exhibits enhanced accuracy while maintaining computational efficiency, making it a promising solution for practical X-ray security inspection systems.
Enhanced Fault Tolerant Control for Double Fed Asynchronous Motor Drives in Electric Vehicles Roubache, Toufik; Merzouk, Imad; Chaouch, Souad
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.v5i3.1913

Abstract

In the dynamic realm of electrical system traction, when Electric Vehicles (EVs) operate at various speeds or require high levels of accuracy and reliability in propulsion, malfunctions or faults might occur. Therefore, the drive system must be capable of detecting, estimating, and accommodating these faults using the designed controllers. This paper proposes an efficient Fault-Tolerant Control (FTC) based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and an integrated Luenberger Observer (LO) for speed tracking control of an EV driven by a Double-Fed Asynchronous Motor (DFAM). The ANFIS controller and LO are employed to play two functions: One for sensorless control and the other for estimating the fault that affect the machine. The performance metrics and accuracy of the ANFIS process are tested using statistical parameters, sush as Root Mean Square Error (RMSE), and convergence analysis. We use a High-Order Sliding Mode Controller (HOSMC), as a nominal control for DFAM. Moreover, the efficacy of the suggested control is demonstrated by comparing its performance with conventional FTC. We have found that ANFIS improves both the precision and responsiveness of the FTC, demonstrating no peak overshoot as well. The obtained results prove that the FTC-based on ANFIS was more enhanced fault estimation accuracy, reduced error, and faster convergence than the conventional FTC methods. Finally, these significant improvments underscore the effectiveness of the suggested algorithm.

Page 34 of 37 | Total Record : 361

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