Showing 46 results for Fuzzy
Raheel Jawad, Rawaa Jawad,
Volume 20, Issue 3 (9-2024)
Abstract
Fire accidents are a disaster that can cause loss of life, property damage and permanent disability to the affected victim. Firefighting is a very important and dangerous job. Firefighters must extinguish the fire quickly and safely to prevent further damage and destruction. Detecting and extinguishing fires is a dangerous task that always puts the lives of firefighters at risk. One of the most effective tools for early fire extinguishing is the firefighting robot. Fire sensing in most industries is absolutely essential to prevent catastrophic losses. Robots with this type of embedded system can save the lives of engineers in industrial sites with hazardous conditions. This project aims to design and implement a solar-powered with artificial intelligent of mobile fire detection robot to detect fires in disaster-prone areas and thus reduce human work effort and level of destruction. Design a robot capable of moving using a rotary motor, finding a flame using a flame sensor, and extinguishing a fire using a water spray using a pump, all of which is controlled by an Arduino Uno microcontroller and programmed using an artificial intelligence (fuzzy) logic technology) using MATLAB, the inputs It has two variations:: flame and gas with three organic functions, each of which has a gas variable (low, medium, high), flame sensor (small, normal, large), and the output is a pump, (pump off , pump on ) with 9 rules. In addition to the experimental setup of the proposed system which demonstrates the performance of sensors (gas, flame) using fuzzy and implemented logic tools. The performance of the solar panels was first tested using MATLAB software as well as experimentally under different weather conditions. The pump's performance is being tested experimentally, and the robot is also being tested to detect and extinguish fires. The process of designing and implementing robotics involves creating mechanical and electrical systems. The results showed the effect of temperature change on the solar panel, as when it increases, the panel’s production capacity decreases, as well as the effect of decreased solar radiation resulting from clouds and other things, and the extent of its effect. Impact on the performance efficiency of solar panels, and observing the pump performance in terms of flow rate and height. Hence, it can be noted that the robot designed in the project is capable of discovering fire sources and extinguishing them using fire-fighting systems equipped with a water tank and a controllable pump to spray the water necessary for the process. From this study, can be concluded that the designed model is able to work according to its initial design with artificial intelligence with the least amount of errors, and therefore it can be applied in industrial applications, avoiding fire damage and extinguishing it when it occurs for the first time.
Ying Foo Leong, Nizaruddin M. Nasir, Suliana Ab-Ghani, Norazila Jaalam, Nur Huda Ramlan,
Volume 21, Issue 2 (6-2025)
Abstract
This paper focuses on the application of a cascaded multilevel inverter, specifically the 5-level multilevel inverter, utilizing a proposed controller known as the FLC-PSO-PI controller. The primary challenge addressed in this research is the precise regulation of output voltage in the multilevel inverter during load variations while meeting voltage harmonic and transition requirements as per industry standards, which are the 10 % voltage limit recommended by IEC and 8 % of total harmonic distortion (THD) by IEEE. An innovative solution is proposed by integrating PSO and FLC to dynamically adapt the controller in real-time, ensuring stable and accurate output voltage regulation. The proposed controller is designed and simulated using MATLAB/Simulink, and its performance is compared with PSO-PI and no controller under various load conditions. The results demonstrate that the FLC-PSO-PI controller significantly enhances output voltage regulation were achieving the desired peak voltage and low THD across different load scenarios, including half load to full load (0.8 %) and no load to full load (0.89 %). Furthermore, the FLC-PSO-PI controller exhibits superior transient response characteristics, such as reduced overshooting (2.89 %), faster rise time at 36.946 µs, and satisfactory settling time at 151.014 µs. This research contributes to the advancement of multilevel inverter technology and its potential applications in renewable energy systems, motor drives, and grid-connected devices. The proposed FLC-PSO-PI controller offers a promising solution for precise voltage regulation in multilevel inverters, enhancing their performance and enabling widespread adoption in various industrial sectors.
Nurul Husna Abd Wahab, Mohd Hafizuddin Mat, Norezmi Md Jamal, Nur Hidayah Ramli,
Volume 21, Issue 2 (6-2025)
Abstract
In islanded microgrids, circulating currents among parallel inverters pose significant challenges to system stability and efficient power distribution. Traditional droop control methods often struggle to manage these currents effectively, leading to inefficiencies and potential system damage. This study introduces an advanced fuzzy-robust droop control strategy that integrates fuzzy logic with robust droop control to address these challenges. By incorporating fuzzy logic, the proposed strategy enhances the adaptability of droop control to varying system conditions, improving the management of circulating currents and ensuring more accurate power sharing among inverters. Comprehensive mathematical modeling and extensive simulation analyses validate the performance of this control strategy. The results show that the fuzzy-robust droop control method significantly outperforms conventional approaches, achieving up to a 70% reduction in circulating currents. This improvement leads to a substantial reduction in power losses and enhances the dynamic response under varying load conditions. Additionally, the strategy improves voltage and frequency regulation, contributing to the overall stability and reliability of the microgrid. The findings provide a robust solution to the longstanding issue of circulating currents, optimizing microgrid operations, and paving the way for more efficient and resilient distributed energy systems. The advanced control strategy presented in this study not only addresses critical challenges but also demonstrates the potential for innovative methodologies to meet the growing demands of future energy infrastructures, where reliability and efficiency are essential.
Kumuthawathe Ananda-Rao, Steven Taniselass, Afifah Shuhada Rosmi, Aimi Salihah Abdul Nasir, Nor Hanisah Baharudin, Indra Nisja,
Volume 21, Issue 2 (6-2025)
Abstract
This study presents a Fuzzy Logic Controller (FLC)-based Maximum Power Point Tracking (MPPT) system for solar Photovoltaic (PV) setups, integrating PV panels, a boost converter, and battery storage. While FLC is known for its robustness in PV systems, challenges in battery charging and discharging efficiency can affect performance. The research addresses these challenges by optimizing battery charging, preventing overcharging, and enhancing overall system efficiency. The FLC MPPT system is designed to regulate the battery's State of Charge (SOC) while evaluating system performance under varying solar irradiance and temperature conditions. The system is modeled and simulated using MATLAB/Simulink, incorporating the PV system, MPPT algorithm, and models for the PV module and boost converter. System efficiency is assessed under different scenarios, with results showing 97.92% efficiency under Standard Test Conditions (STC) at 1000 W/m² and 25°C. Additionally, mean efficiencies of 97.13% and 96.13% are observed under varying irradiance and temperature, demonstrating the effectiveness of the FLC MPPT in regulating output. The system also extends battery life by optimizing power transfer between the PV module, boost converter, and battery, ensuring regulated SOC.
Majid Golkhatab, Aref Shahmansoorian, Mohsen Davoudi,
Volume 21, Issue 4 (11-2025)
Abstract
This paper presents a novel hybrid navigation approach for autonomous mobile robots in obstacle-rich environments. The method integrates artificial potential fields for obstacle avoidance with fuzzy logic for path planning, which is optimized by a genetic algorithm to enhance adaptability and robustness to sensor uncertainties. Experimental results demonstrate significant improvements over traditional artificial potential field methods and are validated through real-time implementation on a ROS-based mobile robot.
Zead Mohammed, Basil Mahmood, Saad Saeed, Aws Hazim,
Volume 22, Issue 0 (3-2026)
Abstract
A mobile robot must be autonomous to avoid obstacles while traveling towards the target. The problem of dynamic obstacle avoidance is still a significant challenge. Reactive mobile robot navigations handled this problem, but using a single-stage module leads to a deficiency and a limitation in performance. This paper proposes combining an adaptive neuro-fuzzy inference system and a neural network. The data for obstacle severity classification were used to train the Bayesian regularization Back-Propagation Neural Network. The relative velocity and distance between the mobile robot and obstacles determine the zone. Zone 1 is dangerous, and Zone 5 is safe. This paper uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to avoid obstacles during the mobile robot's motion and to avoid collision. Based on our empirical study, we consider three essential features in this paper: the relative speed, distance, and angle between the robot and the obstacle as inputs to the obstacle avoidance system ANFIS. The output was a suggested steering angle and speed for the mobile robot. The simulation results for the tested cases show the capability of the proposed controller to avoid static and dynamic obstacles in a fully known environment. Our results show that the Adaptive Neuro-Fuzzy Inference System enhances the proposed controller's performance, reducing path length, processing time, and the number of iterations compared.
