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Journal of Fuzzy Systems and Control (JFSC)
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29866537     DOI : https://doi.org/10.59247/jfsc.v1i1.24
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Journal of Fuzzy Systems and Control is an international peer review journal that published papers about Fuzzy Logic and Control Systems. The Journal of Fuzzy Systems and Control should encompass original research articles, review articles, and case studies that contribute to the advancement of the theory and application of fuzzy systems and control, and their integration with other technologies, such as artificial intelligence, machine learning, and optimization.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 78 Documents
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Nonlinear Control Law Design for Inverted Pendulum Systems via RBF Neural Networks Van Khuong, Huynh; Chiem, Nguyen Xuan; Obukhov, Alexander
Journal of Fuzzy Systems and Control Vol. 3 No. 2 (2025): Vol. 3, No. 2, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i2.314

Abstract

This paper presents the design of a nonlinear control law based on the Backstepping method combined with Radial Basis Function (RBF) neural networks to ensure the stability of an inverted pendulum system with unknown model parameters. The control design is developed using a general form of the system’s mathematical model, in which the unknown nonlinear functions are approximated by RBF neural networks. Experimental results conducted on the STM32F4 embedded platform demonstrate that the proposed approach not only guarantees system stability but also verifies the effectiveness and practical applicability of the control law.
Optimizing Hybrid LiFi Communication Systems Using Fuzzy Reinforcement Learning for Enhanced Network Performance Azeez, Fatimah Abdulameer; Hamza , Bashar Jabar
Journal of Fuzzy Systems and Control Vol. 3 No. 2 (2025): Vol. 3, No. 2, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i2.316

Abstract

Light Fidelity (LiFi) technology has emerged as a pivotal solution for high-speed data transmission in modern communication networks. However, its limitations, such as signal obstruction and coverage gaps, necessitate integration with hybrid systems to ensure seamless connectivity. This study introduces a novel Fuzzy Reinforcement Learning (FRL) algorithm to optimize hybrid LiFi communication systems, addressing critical challenges like handover inefficiency, load imbalance, and dynamic environment adaptation. The proposed FRL framework combines fuzzy logic to manage uncertainties in user mobility and channel conditions with reinforcement learning to dynamically adapt network parameters, ensuring optimal performance. Through comprehensive simulations and real-world validations, the hybrid system demonstrates significant improvements in throughput (4.8 Gbps), handover latency (20 ms), and coverage (100% user connectivity) compared to standalone LiFi and traditional RF-based networks. Key contributions include non-linear decision-making, long-term performance optimization, and scalable deployment strategies for next-generation wireless systems. The results highlight the potential of FRL-optimized hybrid LiFi networks to overcome current bandwidth constraints, offering a robust solution for 6G and IoT applications. This work bridges the gap between theoretical advancements and practical implementation, paving the way for energy-efficient, high-performance communication systems.
Trajectory Control for Double-Linked Parallel Rotary Inverted Pendulum Mai, Thanh-Truong; Le, Tuan-Thuong; Le, Hong-Quang; Than, Bao-Duy; Trinh, Hoang-Thien-Hung; Nguyen, Dinh-Truc Nguyen; Tran, Hung-Thinh; Le, Tran-Quang-Huy; Hoang, Tri-Dung; Duong, Sy-Luan; Tran, Hoang-Chinh; Nguyen, Ha-Duy; Dong, Thi-My-Linh
Journal of Fuzzy Systems and Control Vol. 3 No. 2 (2025): Vol. 3, No. 2, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i2.317

Abstract

The rotary inverted pendulum (RIP) is a benchmark nonlinear underactuated system commonly used in control research, with various extensions such as multi-link and parallel configurations developed to increase complexity and evaluate advanced controllers. This paper presents a hybrid control strategy combining Linear Quadratic Regulator (LQR) and a Genetic Algorithm (GA) for stabilizing and tracking control of a rotary double-linked parallel inverted pendulum (RDPIP), a nonlinear under-actuated single input-multi output (SIMO) system. The LQR controller is designed based on a linearized state-space model at the TOP-TOP equilibrium point. To enhance performance, the weighting matrices Q and R are optimized using GA with a fitness function minimizing trajectory error. Simulation results demonstrate that the GA-optimized controller (LQR 2) achieves superior performance compared to the trial-based LQR (LQR 1), with a reduced settling time of 0.5 seconds, lower oscillation amplitudes, and improved tracking of reference signals under sinusoidal and pulse disturbances. Specifically, the pendulums reached steady state within 2–3 seconds, and the arm settled within 6 seconds. These findings confirm the effectiveness of a hybrid strategy and robustness of the proposed hybrid approach for RDPIP control, laying a foundation for future implementation in real-world applications.
Design of an Indirect Adaptive Controller Based on Fuzzy Logic Control for Linear Cascade Systems Affected by Bounded Unknown Disturbances Long, Hoang Duc
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.320

Abstract

This paper presents a novel indirect adaptive control scheme that integrates fuzzy logic with a virtual integral-based adaptive controller to enhance the tracking performance of linear cascade systems under bounded unknown disturbances. The proposed controller builds upon the established indirect adaptive control framework, employing a virtual state algorithm but augments it with a fuzzy inference mechanism that dynamically adjusts a key control parameter to improve robustness and adaptability. Gaussian membership functions and a Sugeno-type fuzzy inference system are employed to fine-tune the gain parameter based on real-time tracking error and its derivative. The control law incorporates parameter adaptation, a saturation function to replace discontinuous sign operations, and a fuzzy-tuned gain to mitigate chattering and improve transient response. Simulation results under severe disturbances demonstrate significant improvements in tracking accuracy and control smoothness. Specifically, the proposed fuzzy-based controller reduces steady-state tracking error by over 40%, minimizes control chattering, and maintains robust performance under disturbance amplitudes up to 185 units-conditions that severely degrade the performance of the non-fuzzy indirect adaptive controller. The effectiveness of the proposed algorithm is shown by handling model uncertainties and external perturbations in a two-level linear cascade system.
Phishing Website Detection via a Transfer Learning based XGBoost Meta-learner with SMOTE-Tomek Agboi, Joy; Emordi, Frances Uche; Odiakaose, Christopher Chukwufunaya; Idama, Rebecca Okeoghene; Jumbo, Evans Fubara; Oweimieotu, Amanda Enaodona; Ezzeh, Peace Oguguo; Eboka, Andrew Okonji; Odoh, Anne; Ugbotu, Eferhire Valentine; Onoma, Paul Avwerosuoghene; Ojugo, Arnold Adimabua; Aghaunor, Tabitha Chukwudi; Binitie, Amaka Patience; Onochie, Christopher Chukwudi; Ejeh, Patrick Ogholuwarami; Nwozor, Blessing Uche
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.325

Abstract

The widespread proliferation of smartphones has advanced portability, data access ease, mobility, and other merits; it has also birthed adversarial targeting of network resources that seek to compromise unsuspecting user devices. Increased susceptibility was traced to user's personality, which renders them repeatedly vulnerable to exploits. Our study posits a stacked learning model to classify malicious lures used by adversaries on phishing websites. Our hybrid fuses 3-base learners (i.e. Genetic Algorithm, Random Forest, Modular Net) with its output sent as input to the XGBoost. The imbalanced dataset was resolved via SMOTE-Tomek with predictors selected using a relief rank feature selection. Our hybrid yields F1 0.995, Accuracy 1.000, Recall 0.998, Precision 1.000, MCC 1.000, and Specificity 1.000 – to accurately classify all 3,316 cases of its held-out test dataset. Results affirm that it outperformed benchmark ensembles. The study shows that our proposed model, as explored on the UCI Phishing Website dataset, effectively classified phishing (cues and lures) contents on websites.
EcoSMEAL: Energy Consumption with Optimization Strategy via a Secured Smart Monitor-Alert Ensemble Aghaunor, Tabitha Chukwudi; Agboi, Joy; Ugbotu, Eferhire Valentine; Onoma, Paul Avweresuoghene; Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Eboka, Andrew Okonji; Ezzeh, Peace Oguguo; Geteloma, Victor Ochuko; Binitie, Amaka Patience; Orobor, Anderson Ise; Nwozor, Blessing Uche; Ejeh, Patrick Ogholuwarami; Onochie, Christopher Chukwudi
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.319

Abstract

The global demand for automation that seeks the efficient consumption and usage of energy via the adoption of embedded-fit management solutions that yield improved performance with reduced consumption has become the new norm. These explore sensor-based units in their own right with eco-friendly platforms that raise germane environmental, health, and consumption regulation(s) concerns that have today become a global issue, even when they proffer improved life standards that replace traditional solutions. Our study posits an embedded sensor design to observe environmental conditions associated with energy consumption by residential or home appliances. It utilizes a machine learning scheme and algorithm to analyze the total energy consumed by each appliance and delivers optimal consumption that reduces energy waste. The system was tested across multiple parameters and found to yield desired effectiveness, reliability, and efficiency. Our utilization of the ESP8266 and ThingSpeak is able to handle extensive inputs without significant delays or data losses. Results affirms the system ability to maintain stable performance even with more devices connected to the unit.
High Speed Automatic Cartoning Machine Tran, Nguyen-Tuong-Quang; Pham, Quang-Tuan-Vu; Le, Thi-Hong-Lam; Cai, Minh-Hien; Nguyen, Xuan-Khai; Nguyen, Thanh-Phuong; Tran, Dinh-Nguyen; Nguyen, Huu-Thinh; Nguyen, Van-Huu-Nhan; Than, Gia-Huy
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.322

Abstract

High-speed automatic cartoning machines are increasingly used in modern manufacturing for enhanced productivity and packaging quality. This study presents the design and implementation of a compact, student-friendly, and cost-effective automatic cartoning system based on the Siemens S7-1200 PLC and advanced motion control techniques. The system includes a stepper motor-driven conveyor, an AC servo for precise positioning, and an automated glue spraying unit, all managed via TIA Portal V17. Experimental evaluation shows the prototype achieves a packaging rate of 10 boxes/min, position accuracy of ±0.4 mm, system cycle time of 2.0 ± 0.3 s, glue application error below 1.2%, mean error recovery time of 3.5 s, machine up-time of 99.1% over 8 hours, user setup time <10 min, and energy consumption of 35W per cycle. Comparison with commercial solutions indicates comparable performance at 40% lower cost. The results confirm the effectiveness of the proposed model for education and suggest potential for further optimization in fault tolerance and mechanical robustness.
Comparative Analysis of PID-Driven Data-Based and PSO-Tuned Fuzzy Membership Functions for Robotic Manipulator Control Chotikunnan, Phichitphon; Khotakham, Wanida; Imura, Pariwat; Chotikunnan, Rawiphon; Wongkamhang, Anantasak; Thongpance, Nuntachai
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.335

Abstract

Robotic manipulators require control systems that are both responsive and precise in order to ensure accurate tracking and stability in dynamic environments. Conventional fuzzy logic controllers that are based on proportional integral derivative (PID) methods frequently encounter difficulties in achieving fast response, minimal steady-state error, and low overshoot. This study presents a comparative evaluation of a PID-driven data-based fuzzy logic controller and a particle swarm optimization (PSO) tuned fuzzy logic controller for a three-axis robotic manipulator implemented in Simulink. Both controllers used Gaussian membership functions within a Mamdani inference structure. The PSO algorithm was employed to optimize fuzzy input-scaling gains using a composite performance index that incorporated absolute error, control effort, overshoot penalty, and steady-state error. The simulation results indicate that the PSO-tuned controller consistently outperformed the benchmark. On the R-axis, it shortened rise and settling times and reduced overshoot, mean absolute error (MAE), and root mean square error (RMSE). On the T-axis, response speed and error values improved, although overshoot increased, indicating a trade-off between speed and stability. On the Z-axis, the PSO controller achieved a substantial decrease in overshoot, lower error metrics, and faster stabilization. Overall, the PSO-based tuning process preserved steady-state stability while improving transient performance on all axes. These findings show that metaheuristic optimization is an effective and practical method for enhancing fuzzy logic controllers in robotic manipulators. This approach has potential applications in precision manufacturing, service automation, and surgical robotics.

Page 8 of 8 | Total Record : 78

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