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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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Classifying Gait Disorder in Neurodegenerative Disorders Among Older Adults Using Machine Learning Rahman, Kazi Ashikur; Shair, Ezreen Farina; Abdullah, Abdul Rahim; Lee, Teng Hong; Ali, Nursabillilah Mohd; Zakaria, Muhammad Iqbal; Al Betar, Mohammed Azmi
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.v5i2.1722

Abstract

Gait disorders are a significant concern for older adults, particularly those with neurodegenerative diseases such as Parkinson’s disease, Huntington’s disease, and Amyotrophic Lateral Sclerosis. Accurately classifying these conditions using gait data remains a complex challenge, especially in older populations, due to age-related changes in gait patterns, comorbidities, and increased variability in mobility, which can obscure disease-specific characteristics. This study explicitly classifies neurodegenerative diseases in older adults by analysing age-specific gait force data. Continuous Wavelet Transform (CWT) was utilised for advanced feature extraction, capturing both temporal and spectral signal characteristics. Classifiers including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Multilayer Perceptron (MLP) were employed. The results demonstrated that SVM achieved an accuracy of 87.5%, outperforming RF and MLP, which achieved 83.3% and 50.0%, respectively. These findings underscore the importance of using tailored machine learning approaches to improve the diagnosis and management of neurodegenerative diseases in older adults. The potential for real-world application includes integration into clinical settings, enabling early detection and personalized interventions for individuals with gait disorders.
Performance Enhancement of BLDC Motor Drive Systems Using Fuzzy Logic Control and PID Controller for Improved Transient Response and Stability Abdullah, Zainab B.; Shneen, Salam Waley; Dakheel, Hashmia Sharad
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.v5i2.1882

Abstract

Currently, systems generally need control units, which requires designing them to analyze the behavior of the system when there are suitable characteristics of the motor according to the required application. The electric motor is very important in many applications and is widely used because of the high-efficiency mechanical power, small sizes, and relatively high torques that these electrical machines have. Improving the performance of systems requires control units, which are of the types of traditional PID, expert Fuzzy, and intelligent control systems. Two systems were proposed, a system that relies on a traditional control unit and a system that relies on fuzzy logic to improve and raise the efficiency of performance and handle system fluctuations resulting from disturbances and different operating conditions. Simulation tests were conducted using MATLAB. The effectiveness of the proposed controllers is evaluated through measurement criteria including efficiency improvements, torque ripple reduction, or settling time. Simulation results for both the closed-loop system using the conventional controller and the expert controller showed that the improvement in system performance can be determined according to criteria that include response speed as well as the overshoot and undershoot rates. Specifically, the settling time using the conventional controller was 3.05 msec. The rise time using the conventional controller was 205.406 msec, while using the expert controller it was 205.406 msec. The overshoot rate (%) using the conventional controller was 18.452%, while using the expert controller it was 6.989%. The undershoot rate using the conventional controller was 6.633%, while using the expert controller it was 1.987%.
Real-Time Pose Estimation for Autonomous Vehicles Using Probabilistic Landmark Maps and Sensor Fusion 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.v5i2.1851

Abstract

This study introduces a robust and accurate method for estimating autonomous vehicle position, facilitating safe navigation in urban and highway settings. The proposed technique employs a probabilistic particle filter framework, which, unlike approaches constrained by Gaussian assumptions, represents probability densities as samples, enabling more flexible position estimation. A key innovation lies in integrating a finely tuned Unscented Kalman Filter (UKF) to fuse radar and lidar data specifically for robust detection of pole-like static landmarks, whose positions and associated uncertainties are probabilistically modeled within an offline reference map. The particle filter leverages Bayesian filtering, associating UKF-derived landmark observations with this probabilistic map to refine the vehicle's pose. Broad simulation tests validate the method's effectiveness, achieving a mean localization error of approximately 11 cm in both longitudinal and lateral directions. Furthermore, the system demonstrates robustness, maintaining localization accuracy below 30 cm even with landmark position uncertainties up to 2 meters, and confirms real-time capability exceeding 100 Hz. These findings establish the approach as a reliable and precise solution for autonomous vehicle localization across various scenarios.
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.
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.
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.

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