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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 76 Documents
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Experiment Ball Levitation with Fuzzy PID and PID Implementation Nguyen, Hoang-Thuat; Dao, Anh-Quan; Hoang, Van-Phu-Quy; Nguyen, Quyen-Anh; Dang, Truong-Phu; Tang, Minh-Nam; Le, Vu-Huy; Bui, The-Nam-Vuong; Nguyen, Tien-Dung; Le, Thi-Hong-Lam
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.221

Abstract

The “Ball Levitation” experiment can be easily recognized, like iFly in Singapore, and is greatly integrated into industrial fields such as flow control systems, aerodynamic testing, the oil and gas industry, HVAC systems, etc. Therefore, it is utilized in university laboratories for student exploration of non-linear control technology. The main objective of this experiment is through the position of the ball which is measured by an ultrasonic sensor to execute the PWM of the blower fan in order to control the speed of one so that the ball can be stabilized consistently at a specific height. Despite its uncomplicated model, the challenge of this model is from non-linear effects on the ball and the intricate physics governing its movement. Moreover, the ball is highly responsive to external influences from the blower fan. Consequently, conventional mathematical control methods struggle to handle it, making the simulation and comparison of control algorithms challenging. A Fuzzy-PID controller is meticulously designed to automatically stabilize the ball's position by considering the PID parameters with pre-defined fuzzy rules due to the actual showcase of the model. This setup allows us to experimentally compare the traditional PID controller with the Fuzzy-PID controller. The results reveal notable differences in the performance characteristics of these controllers.
Fuzzy Logic-based PI Controller with PWM for Buck-Boost Converter Al-Dabbagh, Zainab Ameer; Shneen, Salam Waley; Hanfesh, Abduljabbar O.
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.239

Abstract

Electronic power converters have emerged and been widely used as a result of the use of direct current systems, and one of their most important uses is renewable energy such as solar energy. When electrical energy is generated from primary sources such as solar energy, converters can be added to convert to a higher or lower value using a buck-boost converter. If the converter is used and operated within an open-loop system, which is the first proposed test case, it is possible to verify the ability of the converter to convert with a constant current rate according to its function, but it turns out that the conversion, in this case, is in a state of instability, which requires work to add feedback and make the system operate in a system Closed loop is the second test case to reach and ensure a stable state for the system. To ensure the scheduled effort, work is being done to improve the system by adding traditional and expert controllers. Thus, by adjusting the parameters of the controller, acceptable performance can be obtained. It represents a transformer with a controller that maximizes results with accuracy and stability. The controller works to track errors through a sensor. It shows the output value with the appropriate reference value for the transformer output, in addition to the presence of a comparator that detects the error to be an input for the controller, which works according to a working algorithm to implement a compensation state, treat the error and instability, get rid of the deviation, and return to the stable state. Various control methods are implemented to improve performance, including traditional PI and expert Fuzzy, with the best being determined by comparing the system output results, as the simulation showed the superiority of fuzzy logic over traditional in terms of response speed time, rise time, and under and over bypass rates.
Control of Bidirectional DC-DC Converter with Proportional Integral Derivative Septiawan, Fadlilah Reza; Tahtawi, Adnan Rafi Al; Ilman, Sofyan Muhammad
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.241

Abstract

In the bidirectional DC-DC converter (BDD converter), power flow is created in two directions. The topology of the converter has two modes: discharge mode and charge mode. In this discussion, control for both modes is done using an external switch. This study discusses points for planning a bidirectional DC-DC converter using the MATLAB / Simulink application and implementing equipment using PID control embedded in the Arduino UNO-type microcontroller device. The converter design in the MATLAB / Simulink application with two modes uses PID control, however, the PID method can only be done in discharge mode in the experimental stage. In obtaining PID parameters using Ziegler-Nichols tuning 1. The response results have been designed in the MATLAB / Simulink application for both modes, which have a rise time value of less than 0.2 seconds, a settlement time value of less than 1 second, and a steady-state error of less than 2%. The results of the hardware experiment in discharge mode have a rise time value of 1 second, a settlement time of 2 seconds, and a steady-state error of 0.8%. The hardware experiment response is slower than the simulation, and the steady state error is larger than the simulation. The charging method can be carried out with a current value of -0.1A.
Speed Control of 3 Phase 1.5 kW Induction Motor using VSD LS SV015IG5A-2 with Proportional Integral Anti-Windup Method Tahtawi, Adnan Rafi Al; Yahya, Sofian; Elbizzar, Passya; Ilman, Sofyan Muhammad
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.242

Abstract

The industrial world in Indonesia is experiencing increasing development. In general, most of the tools in the industrial world use electric motors as the main drive. Induction motors are alternate current (AC) electric motors that are most widely used to support performance in the industrial world. Factors that make induction motors widely used in the industrial world are due to high efficiency and performance, size that is not too large, easier maintenance, and does not cost much. The drawback of the induction motor itself is that controlling the speed of the induction motor is not easy and includes a non-linear motor. Therefore, the right technology is needed to regulate the speed of the induction motor to remain stable when given a change in load. The research conducted is the speed regulation of a 220 volt 1.5 kw 3 phase induction motor by adjusting the frequency using Variable Speed Drive LS SV015IG5A-2 with Arduino-based PI Anti-windup control. This control aims to get a constant 3-phase induction motor speed with a speed of 1200 Rpm when given a loading of 1-8 Nm with a maximum speed error value of ±6%, maximum rise time of 10s, maximum settling time of 10s. PI Anti-windup will reduce the integral calculation so that the PI value does not exceed the maximum limit and is less than the minimum limit of control saturation to maintain a better system response and responsiveness to changes in actual values triggered by varying load changes. Based on the test results of the induction motor speed regulation system using the PI Anti-windup method with a value of Kp = 4; Ki = 0.967; Ka = 0.884 which results in an average rise time of 2.12s, settling time of 4.882s, and steady state error of 0.606.
Implementation of Zhang's Camera Calibration Algorithm on a Single Camera for Accurate Pose Estimation Using ArUco Markers Herdiansyah, Junardo; Putra, Febi Ariefka Septian; Septiyanto, Dwi
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.256

Abstract

Pose estimation using ArUco markers is a method to estimate the position of ArUco markers relative to the camera lens. Accurate pose estimation is crucial for autonomous systems to navigate robots effectively. This study aims to achieve an ArUco Marker pose estimation accuracy of at least 95% using a single camera. The method employed to obtain accurate ArUco pose estimation results is by calibrating the camera with the Zhang camera calibration algorithm. This calibration is necessary to obtain the camera matrix and distortion coefficients, thereby enhancing the accuracy of the pose estimation results. The results of this study include achieving a cumulative calibration error of 0.0180 pixels and pose estimation errors at a distance of 50 cm between the marker and the camera lens. The accuracy on the X-axis was 100%, the Y-axis was 100%, and the Z-axis was 99.823%. At a distance of 70 cm, the pose estimation accuracy on the X-axis was 99.349%, on the Y-axis was 99.462%, and on the Z-axis was 99.066%. At a distance of 100 cm, the pose estimation accuracy on the X-axis was 96.349%, on the Y-axis was 97.641%, and on the Z-axis was 99.344%.
Wind Power Forecasting using Type-2 Fuzzy Control and its Optimization based on Artificial Neural Network for Small Scale Wind Power Chatterjee, Arunava
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.259

Abstract

Improving the efficiency and economic feasibility of variable renewable resources, wind speed forecasting can improve the quality of wind energy generation. By using the properties of wind-related factors, this work provides a new model for wind energy forecasting for electrical power generation at an onshore location in India. The model, which employs an Interval Type-2 fuzzy logic system (IT2FS), takes inputs of wind features and forecasts wind power. Further, an artificial neural network (ANN) is chosen as the adjustment model for optimization in the architecture. The neural network begins evaluating its performance using a different number of hidden-layer neurons. The ANN-based hybrid model outperforms other models according to comparisons drawn from statistical indices. The usage of this adjustment model of forecasting is shown to be quite helpful in predicting the wind power for driving fractional kW loads using wind-based generation techniques.
Internet-based Control of Thermo-optical Plant Improvement based on the PID-GWO System Shneen, Salam Waley; Alkhasraji, Jafaar M. Daif; Sulttan, Mohammed Q.
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.257

Abstract

Experimental work systems with remote access to the laboratory are an inseparable part of engineering education at universities. Many important factors contribute to enhancing overall performance in terms of stability, reliability, and robustness. These include time responses, time delays, inverse responses, significant nonlinearities, multivariable interactions, and modeling uncertainties. This work examines four scenarios for the thermo-optical plant: open-loop PID, a close-loop system without control, a close-loop with PID controller, and a close-loop with a Proportional-Integral-Derivative (PID) controller combined with Grey Wolf Optimizer (GWO), with the goal of achieving the optimal response for a remote thermo-optical plant. The results indicate that scenario four exhibits a smaller percentage overshoot compared to the other scenarios, thereby ensuring a larger stability margin. It outperforms the other three scenarios and significantly enhances the system. The response speed demonstrates a greater percentage of improvement (74.05%).
An Application of STM32F4-Embedded ANFIS-Fuzzy Controller for Tower Crane Nguyen, Ngoc-Truong-Son; Dang, Quang-Hai; Nguyen, Dang-Khang; Lam, Duc-Quan; Nguyen, Vo-Hoai-Nam; Le, Nguyen-Phap-Tri; Tran, Nguyen-Khang; Bui, Van-The-Hieu; Nguyen, Thai-Hoa; Le, ThiHongLam
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.260

Abstract

In this paper, we examine tower crane – a MIMO under-actuated system- which is popular in both academia and industry. From a successful PID controller for this model, we design a fuzzy controller that is generated by the ANFIS toolbox from MATLAB. The proposed controller is shown to be viable based on both the simulation and experimental results obtained. In experiments, the angle of load vibrates a maximum of 10 degrees around the set angle and the settling error is a maximum of 1 degree. Also, the settling time of the trolley is a maximum of 12 sec. These results are acceptable. This control method controls positions and decreases the fluctuation of this model. In the hardware platform, STM32F4 Discovery is used as a control board, and it is well-embedded by fuzzy blocks to prove its ability in future intelligent control.
Model Predictive Control for Rotary Inverted Pendulum: Simulation and Experiment Huynh, Phuc-Hoang; Nguyen, Minh-Hanh; Pham, Nguyen-Phat; Duong, Hoang-Viet-Phuc; Nguyen, Huy-Ha; Le, Duc-Chung; Nguyen, Minh-Khoa; Bui, Ngoc-Liem; Le, Nguyen-Phi-Long; Nguyen, Van-Dong-Hai
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.263

Abstract

Rotary Inverted Pendulum (RIP) is one of the simplest nonlinear systems commonly used for validating control algorithms. In this study, two controllers, Model Predictive Control (MPC) and Linear Quadratic Regulation (LQR), are simulated and experimentally validated. These controllers are executed in real-time on a PC, while the STM32F407 chip handles control and data acquisition from the pendulum using a high-speed USB interface. Due to the custom-built nature of this model, there are inaccuracies in the model and parameter identification. However, results show that the MPC controller is better at trajectory tracking and maintaining balance near the set point compared to the LQR controller. On the other hand, the LQR controller responds more robustly to disturbances and external forces, highlighting distinct differences between MPC’s optimization over each prediction horizon and LQR’s single-solution approach for the entire prediction horizon.
Stacked Learning Anomaly Detection Scheme with Data Augmentation for Spatiotemporal Traffic Flow Binitie, Amaka Patience; Odiakaose , Christopher Chukwufunaya; Okpor, Margaret Dumebi; Ejeh, Patrick Ogholuwarami; Eboka, Andrew Okonji; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Ako, Rita Erhovwo; Aghaunor, Tabitha Chukwudi; Geteloma, Victor Ochuko; Afotanwo, Anderson
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.267

Abstract

The digital revolution births transformation in many facets of today’s society. Its adoption in transportation to curb traffic congestion in major cities globally advances smart-city initiatives. Challenges of population growth, lack of datasets, and aging infrastructure have necessitated the need for traffic analytics. Studies have estimated an associated global annual loss of $583 billion to traffic congestion for 2023. This, caused fuel wastage, loss of time, and increased costs across congested areas. With the cost of building more road networks, cities must advance new ways to improve traffic flow via anomaly detection as an early warning in the flow pattern. Our study posits stacked learning with extreme gradient boost as a meta-learner to help address imbalanced datasets, yield faster model construction, and ensure improved performance via enhanced anomalous data detection.

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