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Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 40 Documents
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Search results for , issue "Vol. 6 No. 4 (2025)" : 40 Documents clear
Integration of PID-MRAC and Novel GCC-C2C for Developing Adaptive Deterministic MPPT Nurcahyo, Sidik; Suyono, Hadi; Hasanah, Rini Nur; Muslim, Muhammad Aziz
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26794

Abstract

This article proposes a new photovoltaic (PV) Maximum Power Point Tracker (MPPT) using PID-MRAC with a novel tracker of Gradual Capacitor-Charging (GCC) and Capacitor-to-Capacitor charge transfer (C2C). The research contribution is omitting the power fluctuation of optimisation-based MPPT and discontinuity or power loss of I-V sweep-based MPPT. GCC regularly and deterministically locates the maximum PV power voltage (Vmpp) by connecting a parallel capacitor to PV only when the PV is isolated from the converter. If one cycle of I-V sweeping is completed, C2C empties the capacitor by transferring its charge to a power supply capacitor to avoid the power-loss problem. A PID and non-inverting buck–boost converter was assigned to regulate the PV output voltage (Vpv) at Vmpp, thus enabling maximum energy harvesting. The Model Reference Adaptive Control (MRAC) adjusts the PID parameters to maintain the MPPT performance. Simulation results show that the MPPT worked well against load and irradiance changes, Iph=2.0A for 0.6s and Iph=3.8A for 1.4s. The GCC-C2C successfully locates Vmpp within 410ms. The PID could regulate Vpv to Vmpp with a settling time of 200ms at the initial stage or less than 10ms at the next stages. The MRAC also successfully tuned the PID parameters during operation. The superiority of this method over the P&O MPPT is its capability to deliver more power at various load power rates. Harvesting efficiency of the proposed MPPT at 5 ohm and 50 ohm loads is 96% and 82%, respectively, while P&O is only 84% and 21%.
Inverse Kinematics Optimization Using ACO, MOA, SPOA, and ALO: A Benchmark Study on Industrial Robot Arms El Mrabet, Aziz; Hihi, Hicham; Laghraib, Mohammed Khalil; Chahboun, Mbarek; Abouyaakoub, Mohcine; Ali, Ali Ait; Amalaoui, Aymane
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26809

Abstract

This study investigates the application of metaheuristic algorithms to solve the inverse kinematics (IK) problem in robotic manipulators, which is often challenging for high-degree-of-freedom systems. The research contribution is the comparative evaluation of four recent metaheuristic algorithms—Ant Colony Optimization, Mayfly Optimization Algorithm, Stochastic Paint Optimizer, and Ant Lion Optimizer—across different robot configurations. A kinematic analysis was conducted on three robotic arms: a 4-DOF SCARA, a 6-DOF ABB IRB 1600, and the dual-arm 15-DOF Motoman SDA20D/12L. For each manipulator, the end-effector pose was optimized by solving the IK problem using the selected algorithms. A total of 30 random target positions were tested within the operational space to ensure diversity in pose challenges; while not exhaustive, this sampling provides statistically informative trends. We evaluate each algorithm based on the number of optimal solutions obtained, the precision of the computed joint configurations, and execution time. The results indicate that the Mayfly Optimization Algorithm consistently delivered the highest precision with relatively low execution time across all robot types. In contrast, the Ant Lion Optimizer showed inconsistent performance in higher-DOF settings. Unlike traditional Jacobian-based or analytical IK methods, metaheuristics offer flexibility in handling complex, nonlinear systems without requiring gradient information. These findings provide practical insight for selecting suitable algorithms in real-world robotic applications.
Adaptive Particle Swarm and Ant Colony Optimization Path Planning for Autonomous Robot Navigation Essaadoui, Alami; Baba, Youssef; Hamed, Oussama; Hamlich, Mohamed; Guemimi, Chafik; EL Kebch, Ali
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26853

Abstract

Path planning in cluttered and uncertain environments remains a significant challenge in robotics, autonomous navigation, and logistics optimization. This paper proposes a novel Adaptive Hybrid PSO-ACO Planner, which synergistically combines Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to compute efficient paths in grid-based environments with static obstacles. Unlike traditional fixed-phase hybrids, our approach features a dynamic switching strategy between PSO and ACO based on real-time convergence behavior, allowing the algorithm to maintain progress and escape local minima. Additionally, adaptive parameter tuning is integrated to enhance the balance between global exploration and local exploitation throughout the search. The switching logic is governed by two criteria: a stagnation threshold that triggers phase transitions and a progress-dependent adaptation mechanism that adjusts search intensities over time. PSO dominates the early search phase, rapidly exploring the solution space, while ACO refines promising paths through pheromone-guided optimization in later stages. The proposed planner also includes a path reconstruction module to ensure solution completeness and robustness. Experimental evaluations on grid-based environments demonstrate that the proposed method consistently achieves higher path quality and faster convergence compared to standalone PSO and ACO approaches. Quantitative results demonstrate notable improvements in path efficiency and overall success rate across a range of obstacle densities. These advancements establish the Adaptive Hybrid PSOACO Planner as a robust and efficient tool for real-time and practical deployment in autonomous robot navigation systems.
A Hybrid Transformer-MLP Approach for Short-Term Electric Load Forecasting Nguyen, Tuan Anh; Tran, Thanh Ngoc
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26960

Abstract

Short-term electric load forecasting plays a vital role in ensuring the stability and efficiency of smart grid operations. However, accurately predicting demand remains challenging due to nonlinearity, volatility, and long-term temporal dependencies in consumption patterns. The research proposes a lightweight hybrid deep learning model that integrates a Transformer encoder with a multi-layer perceptron (MLP) to enhance prediction accuracy and robustness for short-term load forecasting. The proposed model employs a Transformer to extract long-range temporal features through self-attention mechanisms, while the MLP captures complex nonlinear mappings at the output stage. A real-world electricity load dataset collected from three Australian states (NSW, QLD, VIC) between 2009 and 2014 is used for evaluation. To assess model performance, mean absolute percentage error (MAPE), mean squared error (MSE), and Root Mean Squared Error (RMSE) are used. Experimental results demonstrate that the proposed transformer-MLP model consistently achieves the lowest forecasting error across all regions. MAPE ranges from 0.69% to 0.95%, outperforming standard deep learning models, including LSTM, CNN, and MLP. Despite its shallow architecture and reduced computational complexity, the hybrid model effectively captures both temporal dependencies and nonlinear variations. This study provides a practical, deployable forecasting solution for smart grids. Future work will extend the model to multi-step forecasting, incorporate exogenous variables such as weather and calendar effects, and explore deeper Transformer variants further to enhance prediction accuracy and generalization across diverse load conditions.
Real-Time Prohibited Item Detection in X-ray Security Screening via Adaptive Multi-scale Feature Fusion and Lightweight Dynamic Convolutions Nguyen, Hoanh; Ha, Chi Kien
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27030

Abstract

Prohibited item detection in X-ray security screening is a challenging task due to the diverse shapes, sizes, and materials of concealed objects. In this paper, we propose a novel end-to-end framework, integrating adaptive multiscale convolution blocks (AMC Block) and an adaptive lightweight convolution module (ALCM), to address these challenges with high accuracy and efficiency. The AMC block leverages parallel convolutional paths with varying kernel sizes and dilation rates, enabling the capture of both fine-grained and large-scale features. This multiscale strategy ensures that small items like wires and larger objects such as bags or metallic weapons are equally well-detected. Building on top of multi-stage features extracted by the AMC block, we introduce the ALCM to refine and fuse feature maps at different pyramid levels. The ALCM employs a dynamic weight generator (DWG), which adaptively assigns importance to multiple convolutional kernels based on local content, followed by multi-scale depthwise convolutions (MSDC), a lightweight mechanism that enriches features across scales using parallel convolutions with different receptive fields. This approach enhances spatial context while keeping the parameter overhead minimal. Experimental results on two public large-scale X-ray datasets, OPIXray and HiXray, demonstrate that our method achieves state-of-the-art performance while maintaining real-time inference speed. Specifically, our model achieves 91.2% mAP@0.5 and 78.4% mAP@0.5:0.95 on OPIXray, and 87.3% mAP@0.5 and 73.5% mAP@0.5:0.95 on HiXray, outperforming strong baselines including YOLOv9 and Faster R-CNN. Despite competitive accuracy, our model remains efficient with 92.0 GFLOPs and 42 FPS. Furthermore, we examine the generalizability of our system across varied X-ray imaging settings and discuss failure cases such as false negatives in cluttered environments. These findings highlight the practical applicability of our approach for deployment in real-world security checkpoints, striking a strong balance between detection accuracy and computational efficiency.
Improving PMSM Control Based on Optimizing the Output Membership Function of Fuzzy PI Controller with Weighted Modified Jaya Algorithm Khanh, Pham Quoc; Anh, Ho Pham Huy; Thuyen, Chau Minh
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27080

Abstract

Electric vehicle manufacturers prefer permanent magnet synchronous motors for their drive systems because of their high efficiency and power density. PI controllers are ineffective in variable speed drives because they are only linearized to operate within a specific range. One of the solutions applied is to use fuzzy PI controllers instead of PI controllers. When using fuzzy controllers, the designer must be able to adjust the controller parameters to ensure the required control efficiency. There are many ways to improve the control quality of a fuzzy PI controller, such as changing the fuzzification and defuzzification coefficients, the fuzzy rules, the type of membership function, and the shape of the output membership function. In this paper, the method of changing the shape of the output membership function of the speed controller based on the fuzzy PI controller is used to improve the speed control quality of the PMSM motor. The Jaya algorithm and its variants have proven effective in recent publications. In this paper, the Jaya algorithm is supplemented with hybrid weights when targeting the best and worst values in the proposed population to determine the optimal shape of the output membership function implemented by the proposed weighted modified Jaya optimization algorithm. Experimental results on the physical model show the effectiveness of improving the quality of motor speed control when applying the proposed optimization algorithm compared with the basic fuzzy controller based on error assessment criteria such as integral time absolute error (ITAE), integral square error (ISE), and integral absolute error (IAE).
A Comprehensive Review of EEGLAB for EEG Signal Processing: Prospects and Limitations Pamungkas, Yuri; Rangkuti, Rahmah Yasinta; Triandini, Evi; Nakkliang, Kanittha; Yunanto, Wawan; Uda, Muhammad Nur Afnan; Hashim, Uda
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27084

Abstract

EEGLAB is a MATLAB-based software that is widely used for EEG signal processing due to its complete features, analysis flexibility, and active open-source community. This review aims to evaluate the use of EEGLAB based on 55 research articles published between 2020 and 2024, and analyze its prospects and limitations in EEG processing. The articles were obtained from reputable databases, namely ScienceDirect, IEEE Xplore, SpringerLink, PubMed, Taylor & Francis, and Emerald Insight, and have gone through a strict study selection stage based on eligibility criteria, topic relevance, and methodological quality. The review results show that EEGLAB is widely used for EEG data preprocessing such as filtering, ICA, artifact removal, and advanced analysis such as ERP, ERSP, brain connectivity, and activity source estimation. EEGLAB has bright prospects in the development of neuroinformatics technology, machine learning integration, multimodal analysis, and large-scale EEG analysis which is increasingly needed. However, EEGLAB still has significant limitations, including a high reliance on manual inspection in preprocessing, low spatial resolution in source modeling, limited multimodal integration, low computational efficiency for large-scale EEG data, and a high learning curve for new users. To overcome these limitations, future research is recommended to focus on developing more accurate automation methods, increasing the spatial resolution of source analysis, more efficient multimodal integration, high computational support, and implementing open science with a standardized EEG data format. This review provides a novel contribution by systematically mapping EEGLAB’s usage trends and pinpointing critical technical and methodological gaps that must be addressed for broader neurotechnology adoption.
Lyapunov Truncation for Low-Order Modeling of Linear Time-Invariant Unmanned Rotorcraft Flight Dynamics Le, Ngoc-Hoi; Pham, Van-Cuong; Dang, Dinh-Chung; Nguyen, Thi-Mai-Huong
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27251

Abstract

This study addresses model order reduction for unmanned rotorcraft flight dynamics, specifically focusing on the development of computationally efficient, low-order representations for fourth-order linear time-invariant (LTI) models. The research contribution is a systematic evaluation of the Lyapunov Truncation (LT) algorithm in the context of rotorcraft dynamics, where the need for reduced-order models is motivated by real-time control and simulation requirements in autonomous aerial vehicles. The LT method exploits controllability and observability Gramians to identify dominant state directions, but it inherently relies on the assumptions of linearity and time-invariance. The reduction process yields models of third, second, and first order, which are comparatively assessed using time-domain (RMSE), peak error, frequency-domain (total error), and statistical reliability metrics. Results show that the second-order reduced model achieves a 50% reduction in system complexity, with RMSE as low as 0.0537 rad/s in the lateral-to-pitch channel and relative errors consistently below 200% for all channels. Maximum deviations remain under one unit for most channels, and total frequency-domain error is minimized at this order (1519.48). In contrast, first-order models exhibit RMSEs exceeding 1000% in certain channels and peak deviations above 4 units, highlighting limitations in preserving stability margins and transient behaviors. Overall, the study demonstrates that second-order Lyapunov Truncation achieves the optimal balance between computational efficiency and dynamic fidelity, supporting its adoption for practical control-oriented reduction of LTI unmanned rotorcraft models within their valid operational envelope.
Queen Honey Bee Migration-Based Optimization for Battery Management of Internet of Things Devices in High-Risk Emergency Scenarios Widiatmoko, Dekki; Aripriharta, Aripriharta; Sujito, Sujito
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27285

Abstract

Efficient energy management in Internet of Things (IoT) devices is critical in dynamic, resource-constrained operational environments. This study proposes the Queen Honey Bee Migration (QHBM) optimization algorithm for managing Li-ion battery performance in IoT systems, employing the Shepherd battery model to simulate the nonlinear discharge behavior under varying load conditions. Three simulation scenarios of increasing complexity (5, 10, and 20 monitoring points) are used to represent urban operational dynamics. The performance of QHBM is quantitatively compared with four conventional optimization algorithms seperti Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), and Firefly Algorithm (FA). Results show that QHBM maintains a current range of 3.80–5.20 A and a voltage range of 3.65–3.95 V, with State of Charge (SoC) predictions between 75–98%. It also achieves the fastest computation time (0.42–1.20 seconds) and demonstrates more stable performance under high-load dynamic scenarios compared to the other methods. This approach provides an adaptive and efficient optimization framework to support energy-aware decision-making in IoT systems operating in energy-constrained urban environments.
Hybrid Path Planning for Wheeled Mobile Robot Based on RRT-star Algorithm and Reinforcement Learning Method Pham, Hoang-Long; Bui, Nhu-Nghia; Dang, Thai-Viet
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27678

Abstract

In the field of wheeled mobile robots (WMRs), path planning is a critical concern. WMRs employ advanced algorithms to find out the feasible path from a starting point to a specific destination. The paper proposes efficient and optimal path planning for WMRs, integrating collision avoidance strategies and smoothed techniques to determine the best route during navigation. The proposed hybrid path planning consists of improved RRTstar algorithm and reinforcement learning method. Therefore, the RRT* algorithm employs random sampling in conjunction with a reinforcement learning model to purposefully guide the sampling process towards areas that demonstrate an increased likelihood of successful navigation completion. The proposed RRTstar-RL algorithm generates significantly shorter trajectories compared to the traditional RRT and RRTstar methods. Specifically, the path length with the proposed algorithm is 11.323 meters, while the lengths for RRT and RRTstar are 15.74 and 14.40 meters, respectively. Moreover, the optimization of computation time, especially when using pre-trained data, greatly speeds up the path-finding calculation process. In particular, the time needed to generate the optimal path with the RRTstar-RL algorithm is 2.02 times faster than that of RRTstar and 1.6 times faster than RRT. Finally, the proposed RRTstar-RL algorithm has been successfully verified for feasibility and effectively meets numerous objectives established during simulations and validation experiments.

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