Comparison on the performance of Induction motor control using fuzzy and ANFIS controllers

Simon, R.; Geetha, A.

doi:10.1109/ice-ccn.2013.6528549

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…ANFIS control model in the form of TSK (Takagi Sugeno) which has the simplicity of calculations [25]. This control utilizes fuzzy capabilities in reasoning and neural networks in learning [26]. The Association of Fuzzy IF-THEN rules is used to expand the architecture of ANFIS [27].…”

Section: Design Of Anfis Controllermentioning

confidence: 99%

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Sudiharto

DwiMurdianto²,

Budikarso³

et al. 2022

KINETIK

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Nowadays, water heater is a common household appliance. Water heater can be divided into three types, based on fuel sources: gas, diesel, and electric. Electric water heater is the most common due to its ease of use. The problems that often occur on electric water heater are over-temperature due to user error in setting up the thermostat and inaccurate readings caused by a conventional system control. These problems will cause a surge in power consumption. Over-temperature and conventional control inaccuracies can be overcome using the Artificial Intelligence (AI) control algorithm in the form of an adaptive neuro-fuzzy inference system (ANFIS). The proposed algorithm acts as a control by maintaining the stability of the temperature to obtain more accurate results. An accurate temperature reading can lower power consumption in electric water heater. This study tries to simulate Electric Water Heater temperature control using the ANFIS algorithm until stable readings can be achieved in all temperature settings. Results from disturbance tests in the form of external condition that causes sudden temperature change show that the system can maintain stability with an average error margin of 0.045% and the rate of accuracy of 99.955%.

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Section: Design Of Anfis Controllermentioning

confidence: 99%

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Sudiharto

DwiMurdianto²,

Budikarso³

et al. 2022

KINETIK

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“…The combination of the two will complement each other's strengths and weaknesses. Several studies have been carried out to see the comparison between ANFIS and a Fuzzy Logic Controller (FLC), the ANFIS results are better than those of an LFC [17,18]. There are also studies on the comparison of ANFIS and an Artificial Neural Network (ANN).…”

Section: Strengths and Weakness Of Anfismentioning

confidence: 99%

Archives of Electrical Engineering

Said,

Nappu,

Asri

et al. 2021

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Lightning is one of the causes of transmission disorders and natural phenomena that cannot be avoided. The South Sulawesi region is located close to the equator and has a high lightning density. This condition results in lightning susceptibility of disturbances to electrical system lines, especially in high-voltage airlines and substations. An Adaptive Neuro-Fuzzy Inference System (ANFIS) will show the Root Mean Square Error (RMSE) based on the membership function type. This journal is to predict the value of the transmission tower lightning density using the ANFIS method. The value of the lightning strike density index can later be determined based on ANFIS predictions. Analysis of the value calculation system of structural lightning strikes in the South Sulawesi region of the Sungguminasa-Tallasa route can be categorized as three characteristics lightning density (Nd). The calculation system results for the value of structural lightning struck in the South Sulawesi region and validated between manual calculations and ANFIS with an average percentage of 0.0554%.

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“…The structure of ANFIS is shown in Fig-2. It is constructed from 5 layers [32], [33] with the following function:…”

Section: Fig-2mentioning

confidence: 99%

Autonomous Intelligent Flight Control of Fixed-Wing Uav Based on Adaptive Neuro-Fuzzy Inference System

Sarhan¹

2016

IJRET

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This paper develops an autonomous flight controller for fixed-wing UAV based on ANFIS. Three ANFIS modules are designed for controlling the altitude, the heading angle, and the speed of the UAV. In this way, the UAV position is controlled in threedimensional space: altitude, longitude, and latitude position. The simulation results show the capability of the designed approach and its very satisfied performance with good stability and robustness against UAV parametric uncertainties and external wind disturbance.

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Comparison on the performance of Induction motor control using fuzzy and ANFIS controllers

Cited by 9 publications

References 7 publications

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Archives of Electrical Engineering

Autonomous Intelligent Flight Control of Fixed-Wing Uav Based on Adaptive Neuro-Fuzzy Inference System

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