cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Alfian Ma'arif
Contact Email
alfian.maarif@te.uad.ac.id
Phone
-
Journal Mail Official
ijrcs@ascee.org
Editorial Address
Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 25 Documents
 1   2   3 
Search results for , issue "Vol 5, No 3 (2025)" : 25 Documents clear
Third-Order Sliding Mode Control of Five-Phase Permanent Magnet Synchronous Motor Using Direct Torque Control Based on a Modified SVM Algorithm Mehedi, Fayçal; Bouyakoub, Ismail; Yousfi, Abdelkader; Reguieg, Zakaria
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.v5i3.1899

Abstract

Direct Torque Control (DTC) is a powerful method for multiphase drive systems, offering significant performance and efficiency gains, but its implementation is challenged by complexities like uncertainties and disturbances. This research addresses these issues, particularly the variable switching frequencies of hysteresis controllers with switching table and the limitations of conventional proportional-integral (PI) controllers in the outer loop, to enhance DTC for superior control in multiphase drives. The study proposes an improved DTC technique for a five-phase permanent magnet synchronous motor (5Ph-PMSM). This strategy integrates a robust nonlinear third-order super-twisting sliding mode control (TOSMC) with a modified space vector modulation (MSVM) algorithm. The MSVM is based on calculating the minimum and maximum of the five-phase voltages, contributing to optimized performance. This proposed DTC-TOSMC-MSVM approach significantly outperforms conventional DTC (DTC-Conv). It achieves tighter control, substantially reducing flux and torque ripple, and minimizing response time. Furthermore, it lowers the total harmonic distortion (THD) and improves disturbance rejection. The merits of the proposed strategy of 5Ph-PMSM are demonstrated through various tests. MATLAB simulations confirm these benefits, showing an 88.88% reduction in speed response time compared to DTC-Conv. Additionally, the proposed method reduces flux ripple by 51.85%, torque ripple by 63.15%, and stator current THD by 61.08%. In addition, the proposed method demonstrates robust performance when faced with changes in machine parameters and load disturbances, making it superior to traditional DTC approaches.
Design an Optimal Nonlinear Fractional Order PI Controller for Controlling Congestion in Network Routers Abood, Layla H.; Abood, May H.; Hammood, Dalal Abdulmohsin
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.v5i3.1894

Abstract

Active Queue Management (AQM) is a mechanism adapted for notifying senders with network congestion traffics before any overflow happens in the queue which is led to loss data. AQM technique can be applicable in different network size in different fields like industrial systems, colleges and government. In this paper, a nonlinear Fractional Order proportional Integral (NLFOPI) controller is proposed for controlling the Active Queue Management (AQM) system in a stable and robust behavior. An intelligent optimization algorithm called Pelican Optimization algorithm (POA) has been chosen for attain optimal system desired response based on tuning the proposed controller gains for minimizing the error depending on the use of Integral time absolute Error as a fitness function to maintain the whole tuning process based on Matlab program. The proposed NLFOPI controller is regarded as one of the fractional order controllers that depend on using one fractional variable for the integral term only, due to this the tuning parameter will be three instead of two also the nonlinear term will give an enhanced robustness that reflected clearly on system performance. The evaluation analysis represented by settling time, peak time, rise time and overshoot value appeared in system response are done, based on comparison with different classical controllers (PI-PID-FOPI) to show the performance of the proposed controller in different scenarios and then a robustness analysis is adopted by varying the desired queue number values in different time period and also by disturbance rejection when add disturbances signals with values ± 100 packets to desired number of queue in two different periods (15-35) sec., the results reflect how does the system faces these tests done efficiently. Based on simulation results, the NLFOPI controller is regarded as the best controller based on its faster peak time value (tp=3.8 sec) with stable response and a smooth rise time value (tr=1.8 sec.) also a fast-settling time (ts=3.4 sec.) is achieved with un noticeable overshoot (0.2%) if it is compared to other controllers then its robust response is appeared by achieving a satisfied stability and robustness.
Dynamic Ball Balancing Using Deep Deterministic Policy Gradient (DDPG)-Controlled Robotic Arm for Precision Automation Lakshmi, K Vijaya; Manimozhi, M; Kumari, J Vimala
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.v5i3.1892

Abstract

This paper presents a reinforcement learning (RL)-based solution for dynamic ball balancing using a robotic arm controlled by the Deep Deterministic Policy Gradient (DDPG) algorithm. The problem addressed is maintaining ball stability under external disturbances in automated manufacturing. The proposed solution enables adaptive, precise control on flat surfaces. The research contribution is a comparative evaluation of DDPG and Soft Actor-Critic (SAC) algorithms for trajectory control and stabilization. A simulated environment is used to train the RL agents across multiple initial ball positions. Key performance metrics-settling time, rise time, overshoot, and steady-state error-are analyzed. Results show DDPG outperforms SAC with smoother trajectories, ~25% faster settling times, and significantly lower overshoot and steady-state errors. Visual analysis confirms that DDPG consistently drives the ball to the center with minimal deviation. These findings highlight DDPG’s advantages in control accuracy and stability. In conclusion, the DDPG-based approach proves highly effective for precision automation tasks where fast, stable, and reliable control is essential.
A Comparative Study of Fuzzy Logic Controller, ANFIS, and HHOPSO Algorithms in the LEACH Protocol for Optimising Energy Efficiency and Network Longevity in Wireless Sensor Networks Shafeeq Bakr, Zaid; Hassan, Reem Falah; Al-Tahir, Sarah O.; Basil, Noorulden; Ma'arif, Alfian; Marhoon, Hamzah M.
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.v5i3.1918

Abstract

This research provides a thorough analysis of the algorithms used in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for Wireless Sensor Networks (WSNs) to apply Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Harris Hawks Optimisation-Particle Swarm Optimisation (HHOPSO). The primary aim of this paper is to compare and measure these methods by how they save energy, prolong the network’s lifetime and choose the best cluster heads. We look at major indicators such as First Node Death (FND) and the number of rounds when 80% and 50% of nodes are still working, by testing 100 simulated network nodes. The HHOPSO is shown to do a better job at keeping node batteries alive and, at length the network in operation than both Fuzzy Logic and ANFIS. Moreover, ANFIS is more effective than Fuzzy Logic, because it can learn better from data. It is found that HHOPSO helps LEACH become more efficient and effective, contributing new information about how to manage energy and network performance in Wireless Sensor Networks. The document shows the effectiveness of advanced algorithms in keeping sensor networks running longer and offers ideas on how to evaluate them in various network settings.
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.
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.
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.
Disturbance Observer-Based Intelligent Control for Trajectory Tracking in Redundant Robotic Manipulators Al-Mola, Mohammed H. A.; Abdelmaksoud, Sherif I.
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.v5i3.1905

Abstract

Redundant robotic manipulators require advanced control strategies to maintain stability and precision in the presence of dynamic disturbances. This study proposes an intelligent control scheme integrating Active Force Control (AFC) with a Proportional–Integral–Derivative (PID) controller to enhance the performance of a two-degree-of-freedom (2-DOF) robotic manipulator. The proposed AFC-PID controller is designed to suppress the effects of external disturbances, including torque noise. Comparative simulations demonstrate that the AFC-PID approach outperforms the conventional PID controller, providing improved stability and tracking accuracy in both manipulator links. Moreover, it compared with the Sliding Mode Control (SMC) control to verify the efficiency of the proposed controller. Quantitatively, the Integral Square Error (ISE) improvements compared to PID for link 1 and link 2 are 82.83% and 65.57%, respectively. Under disturbance conditions, performance gains are also observed, with ISE reductions of 86.2% and 65.36% for links 1 and 2. These results confirm the robustness and effectiveness of the proposed controller in maintaining consistent performance under challenging conditions. This is a significant improvement, reflecting the superiority over the conventional systems.
Formulation of a Lyapunov-Based PID Controller for Level Control of a Coupled-Tank System Kamarudin, Muhammad Nizam; Md Rozali, Sahazati; Azam, Sazuan Nazrah Mohd; Hairi, Mohd Hendra; Zakaria, Muhammad Iqbal
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.v5i3.1947

Abstract

This manuscript proposes a Proportional-Integral-Derivative (PID) control algorithm based on Lyapunov stability criteria. To verify the technique, the study is further extended to investigate its feasibility in controlling the liquid level of a coupled-tank system. A comparative study is conducted with the well-established Ziegler-Nichols tuning technique, known for its rapid and aggressive response. While Ziegler-Nichols often achieves quick tuning, it is prone to instability or degraded performance, particularly in systems with slow dynamics, such as the coupled-tank system. The results demonstrate the practical viability of the Lyapunov-based PID approach. The findings show that the Lyapunov-PID controller significantly outperforms the Ziegler-Nichols PID, achieving a 33.63% reduction in overshoot and a 45.14% improvement in settling time. These improvements highlight the advantage of incorporating Lyapunov-based criteria in PID design for systems where stability and performance are critical. However, the proposed approach has limitations such as increased computational complexity and the need for abstract tuning effort, along with difficulty in selecting appropriate Lyapunov functions.

Page 1 of 3 | Total Record : 25

 1   2   3 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 5, No 3 (2025) Vol 5, No 2 (2025) Vol 5, No 1 (2025) Vol 4, No 4 (2024) Vol 4, No 3 (2024) Vol 4, No 2 (2024) Vol 4, No 1 (2024) Vol 3, No 4 (2023) Vol 3, No 3 (2023) Vol 3, No 2 (2023) Vol 3, No 1 (2023) Vol 2, No 4 (2022) Vol 2, No 3 (2022) Vol 2, No 2 (2022) Vol 2, No 1 (2022) Vol 1, No 4 (2021) Vol 1, No 3 (2021) Vol 1, No 2 (2021) Vol 1, No 1 (2021) More Issue