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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 50 Documents
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Search results for , issue "Vol 5, No 2 (2025)" : 50 Documents clear
Evaluating the Effectiveness of Alzheimer’s Detection Using GANs and Deep Convolutional Neural Networks (DCNNs) Pamungkas, Yuri; Syaifudin, Achmad; Crisnapati, Padma Nyoman; Hashim, Uda
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.v5i2.1855

Abstract

Alzheimer’s is a gradually worsening condition that damages the brain, making timely and precise diagnosis essential for better patient care and outcomes. However, existing detection methods using DCNNs are often hampered by the problem of class imbalance in datasets, particularly OASIS and ADNI, where some classes are underrepresented. This study proposes a novel approach integrating GANs with DCNNs to tackle class imbalance by creating synthetic samples for underrepresented categories. The primary focus of this research is demonstrating that using GANs for data augmentation can significantly strengthen DCNNs performance in Alzheimer's detection by balancing the data distribution across all classes. The proposed method involves training DCNNs with both original and GAN-generated data, with data partitioning of 80:10:10 for training/ validation/ testing. GANs are applied to generate new samples for underrepresented classes within the OASIS and ADNI datasets, ensuring balanced datasets for model training. The experimental results show that using GANs improves classification performance significantly. In the case of the OASIS dataset, the mean accuracy and F1 Score rose from 99.64% and 95.07% (without GANs) to 99.98% and 99.96% (with GANs). For the ADNI dataset, the average accuracy and F1 Score improved from 96.21% and 93.01% to 99.51% and 99.03% after applying GANs. Compared to existing methods, the proposed GANs + DCNNs model achieves higher accuracy and robustness in detecting various stages of Alzheimer's disease, particularly for minority classes. These findings confirm the effectiveness of GANs in improving DCNNs' performance for Alzheimer's detection, providing a promising framework for future diagnostic implementations.
Investigation and Design of High Efficiency Quadrature Power Amplifier for 5G Applications Taha, Faris Hassan; Hussein, Shamil H.; Yaseen, Mohammed T.; Fadhil, Hilal A.; Assi, Saad A.; Desa, Hazry; Imran, Ahmed Imad; Radhi, Ahmed Dheyaa; Almulaisi, Taha
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.v5i2.1881

Abstract

The rapid rise of the high data rate requirements in modern wireless communications, which include Wi-Fi, LTE, and 5G, demands that appropriate linear and efficient transmitter architecture gets designed. The increased power amplifier (PA) efficiency in the output power back-off (OPBO) is one of the major challenges because it is difficult to achieve PA power efficiency and linearity at the same time. The current study provides design and simulation of a Quadrature Power Amplifier (QPA) for application in 5G in the 5.8 GHz band using 120nm CMOS technology. The proposed QPA system combines Envelope Elimination and Restoration (EER) technique with direct I and Q signal modulation, quite a different solution from the “conventional” approaches of EER and represents very a bandwidth efficient one. Hard-switching drivers as well as the optimized matching networks are used by the system to ensure that there is high power transfer capability and low distortion. In the design process the source impedance is optimized using a source pull simulation and the load impedance is optimized by using a load pull simulation; then, the L-type network is designed to realize optimal matching. For use in implementation, the Rogers RO-5880 material is applied using transmission lines set up through the microstrip techniques in a bid to reduce the losses and parasitic ones. Simulation results show that the QPA obtains a peak output power of 24.35dBm and a power-added efficiency (PAE) of 70% at 5.8 GHz. The best input and output impedances were:  and , respectively. In addition, the envelope and transient simulations prove high-accuracy signal transmission and clean switching quality. This QPA design offers a power-efficient solution with better performance characteristics that makes it an attractive candidate for the future 5G communication systems that are to operate in the 5.8 GHz frequency band.
Active Disturbance Rejection Control for Unmanned Aerial Vehicle Marwan, Hakam; Humaidi, Amjad J.; Al-Khazraji, Huthaifa
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.v5i2.1829

Abstract

This paper presents the design and analysis of a roll motion control system for a vertical take-off and landing of unmanned aerial car (VTOL-UAV) during the hovering flight phase. Ensuring stability and disturbance rejection during hovering is a significant challenge for UAVs, as external disturbances can lead to instability. To address these challenges, this study proposes an Active Disturbance Rejection Control (ADRC) strategy to enhance the system's roll stability and disturbance rejection. The primary contribution is the development of an improved ADRC system by integrating different types of extended state observers (ESO) with a Nonlinear-Proportional-Derivative (NPD) controller. The paper evaluates three ESO types—Linear (LESO), Nonlinear (NESO), and Fractional Order (FOESO)—for system state estimation and disturbance compensation. By combining the best ESO with NPD controller, an enhanced ADRC system is formed and its performance is compared against a conventional Proportional-Integral-Derivative (PID) controller. Numerical simulations performed using MATLAB demonstrate that ADRC significantly improves roll stability and disturbance rejection under both disturbed and undisturbed conditions. The results indicate that the LESO provides the best estimation accuracy, leading to superior system robustness. The ADRC system with LESO outperforms the PID controller in all test cases, particularly in disturbance rejection and stability. The study concludes that ADRC with LESO is an effective solution for improving VTOL-UAV roll motion control during hovering providing a promising approach for future UAV applications in dynamic environments.
A Morphological Context Blocks Hybrid CNN for Efficient Acute Lymphoblastic Leukemia Classification Dubai, Nada Jabbar; Kadhim, Ola Najah; Najjar, Fallah H.
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.v5i2.1824

Abstract

Acute Lymphoblastic Leukemia (ALL) is an aggressive?hematologic malignancy that necessitates early and accurate diagnosis for improved therapeutic efficacy. Although it is a routine practice, the visual blood smear analysis is tedious and?subject to human inaccuracies. This paper proposes a novel morphology-guided deep learning approach called Morphological Context Blocks (MCB)-HyperNet embedding morphological operations into a hybrid CNN architecture. The CNN architectures depend mainly on automatic learning through convolutive filters, so they miss crucial?morphological features that distinguish between leukemic and normal cells. In this study, we propose a deep learning-based approach that directly incorporates morphological dilation?and erosion in the deep learning data pipeline to exploit the potential of morphological feature extraction for our specific task, resulting in enhanced accuracy and reduced diagnostic costs, which ultimately can improve patient outcomes. In addition, the computational efficiency and modularity of the MCB-HyperNet framework facilitate easy adaptation and scalability to many other medical imaging tasks, such as the classification of various diseases, except the classification of?leukemia.  We trained the proposed MCB-HyperNet on different image resolutions from the ALL dataset (168×168, 224×224, 256×256), different batch sizes (16 and 32), and also different training epochs (30, 35, 40, 45, 50) to get the best hyperparameter configuration. The MCB-HyperNet takes advantage of the strong feature extraction ability of ResNet and the light computing resource of MobileNetV3, ultimately obtaining 99.69% accuracy, 98.78% precision, 99.49% sensitivity,?99.12% F1-score, and 99.78% specificity. This new integration greatly enhances the accuracy of early detection, minimizes diagnostic errors, and could have?significant clinical and economic advantages. MCB-HyperNet is a mini CNN, so it shows a good balance between efficiency and accuracy, making scalability and extensibility possible in more medical imaging tasks.
Real-Time Autonomous Vehicle Navigation via Rule-Based Waypoint Selection and Spline-Guided MPC Farag, Wael A.; Fayed, Mohamed
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.v5i2.1879

Abstract

This paper presents a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm aimed at improving autonomous highway navigation. LSPP uniquely combines localized quintic splines with a speed-profile optimizer to generate smooth, dynamically feasible trajectories that prioritize obstacle avoidance, passenger comfort, and strict adherence to road constraints such as lane boundaries. By leveraging real-time data from the vehicle’s sensor fusion module, LSPP accurately interprets the positions of nearby vehicles and obstacles, producing safe paths that are passed to the Model Predictive Control (MPC) module for precise execution. Simulations show LSPP reduces lateral jerk by 30% and computation time by 25% compared to Bézier-based methods, confirming enhanced comfort and efficiency. Extensive testing across diverse highway scenarios further demonstrates LSPP’s superior performance in trajectory smoothness, lane-keeping, and responsiveness over traditional approaches, validating it as a compelling solution for safe, comfortable, and efficient autonomous highway driving.
Powertrain Conversion of a Small Agricultural Tractor from Diesel Engine to Permanent Magnet Synchronous Motor Yaacob, Ahmad Zaki; Jamaluddin, Muhammad Herman; Shukor, Ahmad Zaki; Mansor, Muhd Ridzuan
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.v5i2.1826

Abstract

This paper presents the powertrain conversion of a small diesel-powered tractor into an electric tractor or electric off-road vehicle (EORV), offering a cost-effective alternative to purchasing a new electric model, which may be financially challenging for small-scale farmers. Given that electricity is generally cheaper than diesel fuel in Malaysia, the conversion approach aims to reduce long-term operational costs while maintaining or improving performance. The primary contribution of this work is a systematic and practical method for electric tractor conversion. The process begins with analysing the existing performance and operational requirements of the diesel tractor, followed by the selection of suitable components—namely, the electric motor, battery cells, and other associated systems. These components are then integrated into the tractor, and initial testing was performed. A speed run test was conducted to evaluate the power capability of the converted tractor. Results indicate that the electric motor delivers higher power and speed compared to the original diesel engine. The onboard energy monitoring device recorded a noticeable current spike and voltage sag during acceleration, as expected. The motor power was calculated from the recorded voltage and current data. The data show that the motor output exceeds the rated power of the original engine, suggesting that the system can handle higher loads. Some challenges encountered during the conversion process include the high initial cost, limited availability of components that meet performance requirements, and technical challenges in ensuring the durability and efficiency of the modified drivetrain. In conclusion, further testing under various load conditions is necessary to fully evaluate energy consumption and system performance in real agricultural environments.
Experimental Analysis of Fresnel Lens-Based Solar Desalination Systems with Copper Receivers for Enhanced Thermal and Electrical Performance Mahmood, Abdulkareem Nasir; Azmi, Syahrul Ashikin binti; El-Khazali, Reyad; Çiçek, Adem; Assi, Saad A.; Al-Naimi, Taha Mahmoud; Majdi, Hasan S.; Bektas, Enes; Radhi, Ahmed Dheyaa; Hussain, Abadal-Salam T.
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.v5i2.1852

Abstract

Solar desalination represents a breakthrough technology for creating sustainable freshwater because it meets both the water quality standards and technology efficiency requirements of modern times. The current desalination methods, which depend on fossil fuels, encounter major obstacles regarding their energy requirements and economical performance. The research investigates the improvement of solar desalination performance through coupling Fresnel lens technology with copper-based receivers to maximize thermal characteristics and power generation benefits. This research successfully unites Fresnel lenses of high performance with copper receivers to reach increased steam temperatures alongside power production during the same procedure. The research team performed experimental tests using a system that included four large Fresnel lenses in Sharjah, UAE. Under different operating settings, the system demonstrated its performance by measuring its flow rates together with ambient temperatures and recording the steam output values. The experimental data showed that bigger Fresnel lenses boosted the steam temperature beyond 1000°C as well as pushing pressure levels to 8 bar, which led to remarkable system efficiency benefits. The copper receiver system generated 775 mA DC electric current, which collectively enhanced the system's power efficiency. The tested combination of Fresnel lenses and copper receivers demonstrates an effective way to enhance solar desalination systems, according to observed experimental data. The dualfunction technology combines desalination efficiency improvement with electricity production capabilities to establish a sustainable freshwater production method for arid regions. This investigation creates a basis for developing economical renewable desalination systems going forward.
Design and Implementation of Proportional-Integral Controller for Single Phase Stand-Alone Inverter with an LC-Filter Kareem, Tamarah; Shneen, Salam Waley; Al-Abbasi, Mohammed
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.v5i2.1876

Abstract

Obtaining a sine wave from a DC source using an inverter and a filter is a challenge that requires a suitable design to meet load requirements as operating conditions change. This work aims to develop a suitable design for an LC-type pass-through filter and a suitable design for a conventional controller. A simulation model for the implementation and operation of a single-phase standalone inverter is being developed and designed using Matlab. In this work, the researchers demonstrate the behavior of a simulated system using a single-phase inverter model connected to a 400 V DC power supply. An LC-type filter is also connected to the inverter and the load. Tests are conducted to determine the system's behavior under various conditions. The researchers are interested in changing operating conditions, and the problem of load variations, on the one hand, and transients and the system's return to a steady state, on the other. The researchers propose one method for overcoming system fluctuations using a conventional controller (PI controller). Tests can cover identifying system behavior, and from there, using the controller, an appropriate reference voltage can be set to supply the load. The proposed model consists of a power supply, four IGBT transistor switches to build a single-phase bridge inverter, a filter with an inductor (4.06e-3H) and a capacitor (6.23e-6F), as well as a reference voltage of 200V and 300V, and a load of 55? and 100?. A suitable conventional microcontroller (PIC) is also designed. The feasibility of providing a sine wave with the proposed reference voltage has been verified, proving the feasibility of the model and its potential for future use. Matlab was used to conduct simulation tests of the proposed model, and high performance, accuracy, and quality were obtained at a level suitable for real-time applications.
Fault Detection and Identification Scheme for Boost Converter for Hybrid Vehicles Debab, Nadjib Adil; Bendjedia, Bachir; Bougrine, Mohamed; Djerioui, Ali; Ghellab, Mohamed Zinelaabidine
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.v5i2.1867

Abstract

In a wide range of applications, such as smart buildings, electric vehicles, hybrid systems, and renewable energy, dc dc converters are crucial. The dc dc converters have many topologies, and the boost converter is one of the most important. The problem. The boost converter is connected to other sensitive devices and components, so any fault in the Boost converter will lead to a system issue, which may cause catastrophic damage to humans and related devices. These faults include parameter degradation of passive components, open switch failure, and sensors failures. Goal. The development of a fault detection and identification scheme for a dc-dc boost converter is the main goal of this study. Therefore, it is essential to make sure that the converters are safe from malfunctions and that there are no major accidents or disasters in order for them to carry out their vital jobs. Methodology. The scheme covers a wide range of potential faults, such as parametric degradation of passive components, open switch fault, and sensors failures. We created the scheme as a structured algorithm based on residuals between observers and measurements from the sensors, residuals between open switch fault signature and measurements from the sensors, residuals between assumed values of the sensors and real measurements, and carefully considered thresholds to compare these residuals with. Results. Simulations were used to assess the proposed scheme. The results show the effectiveness of the scheme in detecting and identifying faults quickly and accurately. The originality. of this work lies in the creation of a fault detection and identification scheme using luenberger observers and specific thresholds without the need for additional sensors or devices.
Enhancing MG996R Servo Motor Performance Using PSO-Tuned PID and Feedforward Control Chotikunnan, Phichitphon; Pititheeraphab, Yutthana; Angsuwatanakul, Thanate; Prinyakupt, Jaroonrut; Puttasakul, Tasawan; Chotikunnan, Rawiphon; Thongpance, Nuntachai
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.v5i2.1854

Abstract

The aim of this research is to improve the precision of factory-locked MG996R servo motors, which are frequently employed in biomedical and robotic applications. These motors are characterized by the absence of inherent feedback channels and adjustable internal settings. The proposed technique proposes a non-invasive control strategy that utilizes externally obtained feedback to enable closed-loop control without requiring any modifications to the interior circuitry. The scientific contribution consists of the development of an outer-loop PID control framework that has been optimized using Particle Swarm Optimization (PSO) and enhanced with feedforward compensation. By utilizing the inherent potentiometer, this method ensures the preservation of hardware integrity and enables real-time angle feedback. A model fit of 96.94% was achieved by establishing a second-order discrete-time model using MATLAB's System Identification Toolbox. Particle Swarm Optimization (PSO) was employed to optimize PID improvements offline by minimizing the Integral of Squared Error (ISE). In both experimental and simulated environments, the controller's effectiveness was assessed using 2 rad/s sine wave inputs and a 10° step. The PSO-PID with feedforward controller achieved optimal results, achieving an RMSE of 0.5313° and an MAE of 0.1630° in simulations, as well as an MAE of 0.8497° in hardware step response. The requirement for gain scaling in embedded systems was underscored by the instability of the standalone PSO-PID controller. This method offers a pragmatic, scalable solution for applications such as assistive robotics, prosthetic joints, and surgical instruments. In order to achieve sub-degree precision in safety-critical environments, future endeavors will entail the implementation of adaptive gain tuning and enhanced resolution sensing.

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