Literature Review of the Recent Trends and Applications in Various Fuzzy Rule-Based Systems

Varshney, Ayush K.; Torra, Vicenç

doi:10.1007/s40815-023-01534-w

Cited by 13 publications

(3 citation statements)

References 141 publications

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“…A rule base refers to a collection of decision-making logics that aim to replicate the cognitive process of human judgment-making (Varshney, 2023). The rule base is constructed by utilizing the inputs and their related outputs, specifically determining the desired outputs for a given set of inputs (Tripathy et al, 2022).…”

Section: Design Of Fuzzy Rule Basementioning

confidence: 99%

Enhancing Energy-Efficient Water Pumping with Intelligent Control: A Comparative Study of Advanced Fuzzy Logic and PID Controllers on Simulink/MATLAB

Ramadhan,

Fang

2024

JWS

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Water resources are crucial for social and economic development, human needs, and food production. However, with water scarcity on the rise, minimizing waste during pumping is vital. Using DC motors for pumping offers a solution for sustainable and efficient water management. This study explores intelligent control strategies to enhance energy efficiency in water pumping systems, comparing Advanced Fuzzy Logic and PID Controllers through simulation. The research aims to optimize the control approach for water pumping operations, improving energy efficiency and system performance. Simulation results indicate that both controllers effectively regulate water pumping operations. The Advanced Fuzzy Logic Controller offer better response to setpoints, with minimal overshoot 0,7%, fast settling time 0.052 Seconds, Rise Time 0.048 Seconds, Time Peak 0.05 Seconds and no steady-state error, Compared to PID Controller with Overshoot 10%, Settling Time 0.23 Seconds, Rise Time 0.048 Seconds, Time Peak 0.078 Second and no steady-state error Moreover, the Advanced Fuzzy Logic Controller performs overall better performance in terms of energy efficiency and robustness compared to the PID controller. The findings suggest that the energy efficiency of water pumping systems can be significantly increased by utilizing Advanced Fuzzy Logic Controller approaches, specifically the Advanced Fuzzy Logic Controller. This study contributes to developing control methods for sustainable water management systems with potential uses in agriculture, irrigation, and water distribution.

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Section: Design Of Fuzzy Rule Basementioning

confidence: 99%

Enhancing Energy-Efficient Water Pumping with Intelligent Control: A Comparative Study of Advanced Fuzzy Logic and PID Controllers on Simulink/MATLAB

Ramadhan,

Fang

2024

JWS

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“…2.2 Fuzzy concepts and applications 2.2.1 Fuzzy logic and fuzzy numbers. The fuzzy logic was developed considering mathematical representations that involve uncertainty and imprecision (Guillaume, 2001), as it resembles human reasoning, using information to generate approximate decisions (Varshney and Torra, 2023). It is a tool that models uncertainties and facilitates reasoning for Process risk analysis in aeronautical sector decision-making when complete and precise information is lacking (Bakhtavar et al, 2021;Serrano-Guerrero et al, 2021).…”

Section: Ijqrm 414mentioning

confidence: 99%

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Resende,

Dedini,

Eckert

et al. 2023

IJQRM

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PurposeThis study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.Design/methodology/approachBased on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.FindingsThe results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.Practical implicationsThis study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.Originality/valueThis paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

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“…1. Fuzzification, a process in fuzzy logic systems in which numerical inputs or data that are firm are converted into linguistic variables or fuzzy variables [57], [58].…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

Handling Four DOF Robot to Move Objects Based on Color and Weight using Fuzzy Logic Control

Nugroho,

Setiawan,

Munadi

2023

Journal of Robotics and Control

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Manipulators are increasingly used in industry to improve efficiency in jobs that require precision, long duration, and repetitive work. This research was conducted on a laboratory scale to control manipulators on a pick-and-place system in the product storage and packing area. The object of this research is a four-degree-of-freedom (4-DOF) manipulator controlled using a fuzzy logic system. The hardware used is a conveyor machine to model the product delivery process, Dobot Magician as a 4-DOF manipulator, HX711 load cell serves as a weight sensor, TCS-3200 serves as a color sensor, and Arduino Mega 2560 as a controller. The software used is Dobot Studio as the main program to control the movement of the robot and Matlab to develop the Fuzzy Logic Control (FLC) function, which is embedded in the Arduino. Fuzzy logic control processes weight variables and color variables read by sensors as information data to control the movement of the manipulator. The results showed that the manipulator was able to pick up and place objects according to the path-planning coordinates. The testing data states that the precision and accuracy of the average coordinates of product pick and place against the path planning has an error deviation of 1.8%.

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Literature Review of the Recent Trends and Applications in Various Fuzzy Rule-Based Systems

Cited by 13 publications

References 141 publications

Enhancing Energy-Efficient Water Pumping with Intelligent Control: A Comparative Study of Advanced Fuzzy Logic and PID Controllers on Simulink/MATLAB

Enhancing Energy-Efficient Water Pumping with Intelligent Control: A Comparative Study of Advanced Fuzzy Logic and PID Controllers on Simulink/MATLAB

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Handling Four DOF Robot to Move Objects Based on Color and Weight using Fuzzy Logic Control

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