Journal of Fuzzy Systems and Control

Intelligent Control for 2D-Crane System

2025-12-01T00:46:26+00:00

This paper presents an Intelligent Learning-based Control approach for a 2D Crane System, aiming to evaluate the learning capability of various intelligent techniques based on a baseline Fuzzy Logic Controller (FLC). The initial fuzzy controller is designed for position and sway control, while Genetic Algorithm (GA), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed in simulation to retrain and enhance its performance. Comparative results show that intelligent learning methods can significantly improve system response, reduce overshoot, and increase robustness compared to the original fuzzy controller. Moreover, an experimental setup using the baseline FLC is implemented to verify the practical effectiveness of the fuzzy control approach on a real 2D crane system. The findings highlight the potential of intelligent learning techniques for future real-time implementation.

Study, Design, Modeling, Simulation, and Control Analysis of Single-Phase Rectifier AC-DC Power Converters

2026-01-05T13:07:25+00:00

Many researchers are interested in studying and analyzing electronic power systems due to their importance in providing the electrical quantities needed to meet load requirements. It's worth noting that there are different types of power sources, including direct current (DC) sources such as DC generators, batteries, or solar power, and alternating current (AC) sources such as diesel generators, wind power, and the main grid. Loads vary in that they require DC power, such as lighting, electric motors, and electronic devices. The most widely available power systems are single-phase AC systems, which can power loads of the same type. However, when DC loads, such as electric motors, are present, a rectifier is required to convert the DC current to AC. A rectifier consists of semiconductors such as diodes, thyristors, and transistors, and its output can be controlled by adjusting the operating period of the switches. This study aims to explore the differences between a single-phase half-wave rectifier and a full-wave rectifier. It presents a simulation model of a single-phase rectifier to conduct proposed tests to understand the system's behavior and analyze the simulation results to verify the rectifier's effectiveness in converting alternating current (AC) voltage to direct current (DC) voltage. The results confirm the convertibility capability, making the rectifier one of the important converters that can be used to supply DC loads with electrical power.

Development of an Automated PCB Inspection, Error Statistics, and Classification System

2026-01-26T21:49:15+00:00

In the electronics manufacturing industry, Printed Circuit Boards are critical to electronic devices, and their quality directly affects product performance and reliability. Common assembly defects, such as missing components, misalignment, or wrong parts, must be detected promptly to reduce waste and maintain reputation. In Vietnam, PCB inspection is largely manual, limiting speed, accuracy, and consistency. The system integrates a YOLOv5-based machine vision module for detecting missing and misaligned components, a Siemens S7-1200 PLC for controlling an XY gantry and conveyor system, and a web interface for real-time monitoring. The primary contributions include: a fully integrated cyber-physical prototype suitable for educational and small-scale industrial use; a novel method for component misalignment detection using fiducial-based relative positioning; and seamless communication between vision, control, and HMI modules. Experimental results on two common PCB types, L298N and ULN2003, demonstrate a classification and error detection accuracy of up to 93%. The system achieves a throughput suitable for laboratory and small-batch production, with a positioning accuracy of ±0.5 mm. The system aims to achieve high accuracy, fast processing, and practical applicability in production lines.

An Enhanced PID-Based Motion Control Framework for Autonomous Line-Following Robot

2026-02-12T12:19:48+00:00

PID controller is widely used in automatic control systems because it is simple, reliable, and easy to apply. It is especially suitable for mobile robots, such as line-following robots. The main contribution of this work is an experimental method to tune PID parameters. Instead of using complex algorithms, the parameters are adjusted and tested directly on a real robot. This makes the method easier to apply, especially for low-cost and educational systems. Experiments were conducted to evaluate how PID parameters (Kp, Ki, and Kd) affect the robot’s performance. The robot was tested on different paths, including straight lines, curves, and 90-degree turns. The results show that the optimal parameters are Kp = 65, Ki = 0.1, and Kd = 13. With these values, the robot moves smoothly, responds quickly, and follows the path accurately.

Design and Simulation of DC-AC Power Converters

2026-02-12T01:19:30+00:00

Currently, various types of electronic power converters are used in many fields and industrial applications, one of the most important of which is industrial power converters. They are an essential part of electrical power systems in generating stations, distribution systems, and transmission systems, through their connection to the main grid and sub-grids. Electronic power converters are used in energy generation systems from sustainable development sources, and clean energy is a good example of this. The functions of electronic power converters vary according to the need for them, including changing the type of voltage, current, and frequency, which are electrical quantities. Electronic power converters can be classified into buck, step-down, boost, and step-up. The inverter is one of the types of electronic power converters that works to convert and change the converter input from direct current to a converter output with an alternating voltage. The input of the converter is connected to a DC source such as batteries, solar power, or a DC generator, while the output of the converter supplies AC loads such as a single-phase or three-phase induction motor. The inverter can be single-phase or three-phase, depending on the load to be supplied, and it can be a half-wave or full-wave bridge. The study contributes to implementing and setting appropriate design steps, in addition to building a simulation model to determine the system's behavior during a specific operating period.

Exploring Interval-Valued Fermatean Neutrosophic Tactics for Empowering AI-Driven Financial Risk Frameworks: Compliance Automation, Fraud Detection, and Beyond

2026-02-06T11:09:40+00:00

The financial risk evaluation process, which includes the investigation of the risks related to loans, funding, and trading activities in economic decisions, is greatly utilized in advanced financial systems. However, nowadays in an inconsistent, fast, and digitalized world, conventional risk models are inadequate, particularly when there is ambiguity, inconsistency, or incomplete information provided in the data. In such scenarios, the interference of Artificial Intelligence (AI) is playing a crucial role. Not only are the large data sets dealt with by AI, but also the decision-making processes can be enhanced. Conventional mathematical tools cannot analyze the discipline whose limits are complex, ambiguous, and indeterminate. In this research article, to recognize and analyze these mathematical disciplines, Interval-Valued Fermatean Neutrosophic Numbers (I-VFNNs) are used, an argumentation of modern fuzzy logic. I-VFNNs are particularly mapped out for such circumstances where the information in the data contains uncertainty, ambiguity, or inconsistency. We have used Interval-Valued Fermatean Neutrosophic Numbers (I-VFNNs), an extension of modern fuzzy logic, to identify and analyze these mathematical constraints. In this article, firstly, a list of essential aspects is composed that are affected by Artificial Intelligence in financial risk models, like fraud detection and prevention, stress testing and scenario simulation, automation of regulatory compliance, behavioral risk analysis, enhanced predictive accuracy, dynamic risk modeling, and real-time risk monitoring, etc. All these components we visualized as mathematical disciplines, which are non-probabilistic, irregular, and multifaceted in nature. With the help of I-VFNNs, we portrayed these disciplines in the guise of numbers and demonstrated their influence, intensity, and indeterminacy in accordance with the impact of Artificial intelligence. The results demonstrate that I-VFNNs not only composed ambiguity superiorly, but also refined the disparity between various risk factors. In general, not only are modern ways to constructively assess financial risk models based on Artificial Intelligence (AI) developed, but new approaches in the inspection of indeterminate data using I-VFNNs are furnished in this study. By virtue of this model, in financial organizations, better decisions can be made, accelerate the recognition of risks, and put down the right consideration even in uncertain conditions. In the future, this model can also contribute to other innovative research areas such as international financial policies, large investments, and insurance institutions.

Fuzzy-PID Control for Balancing a Two-Wheeled Inverted Pendulum Robot

2026-03-13T04:39:06+00:00

A two-wheeled inverted pendulum robot (TWIPR) requires continuous control action because its upright position is inherently unstable and highly sensitive to disturbances. This research proposes the use of a fuzzy-PID controller to keep the TWIPR balanced. Although PID has several advantages, its performance can degrade when the system is subjected to changing conditions. To address this, fuzzy logic is applied to enhance the adaptive capabilities of the PID controller. The fuzzy system dynamically generates PID parameters based on predetermined fuzzy rules, effectively maintaining system stability. The fuzzy membership functions used, namely MF3, MF5, and MF7, were compared through no-load and loaded tests. In the no-load test, the fuzzy-PID with MF7 reduced rise time, settling time, overshoot, peak value, and peak time by 1.229%, 0.673%, 86.703%, 7.232%, and 2.952%, respectively, compared with those of the conventional PID. However, the MF3 configuration only excels in overshoot and peak time, while the MF5 configuration only shows improvements in settling time, overshoot, and peak value. Further testing results show that Fuzzy-PID with MF7 provides the most stable performance under load conditions.