Modeling, Design and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Speed Control of Induction Motor

Kusagur, Ashok; Kodad, S. F.; Ram, B. V. Sankar

doi:10.5120/1123-1472

Cited by 48 publications

(26 citation statements)

References 35 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They had created Simscape models of memristor neurons and ANNM for investigating the influence of these factors on the ANNM accuracy. Whereas Kusagur et al [22] used Simscape to introduce a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) to control some parameters of the induction motor; such as speed, torque, flux, voltage and current.…”

Section: Related Workmentioning

confidence: 99%

Modelling Oil Pipelines Grid: Neuro-fuzzy Supervision System

Saeed¹,

Abbod²

2017

IJISA

View full text Add to dashboard Cite

Abstract-One of the major challenges for researchers and governments across the world is reducing resourceswaste or loss. Resources loss can happen if there is not a capable control system that contributes to environmental change. The specific aim is to create user-friendly control and monitoring system to reduce the waste in resources. New Artificial intelligence techniques have been introduced to play an important part in developing such systems.In oilfields, the oil is extracted then distributed via oil pipes until it reaches the end consumer. This operation will occur without a full and complete monitoring for the oil in the pipeline's journey to the provider. Although, the existing oilfield monitoring systems can communicate locally but they will not send information back to the main provider. That means the provider is not aware of the whole circumstances happened in the transportation process. That gives the provider no control on the process. For example, a sudden decision from the main provider to stop transporting to a specific destination or knowing where the leakage is and which pipe is leaking in the pipelines grid. This paper, introduces for the first-time oilfield pipeline Neuro-fuzzy (NF) supervision system using Simscape simulation software package. This system can be the first step solution to keep real time communication between the main provider and the oil transportation process in the oilfields and enables the provider to have full supervision on the oil pipes grid. The simulation supervision system illustrates a clear real-time oilfield pipeline grid that gives the provider the ability to control and monitor pipeline grid and prioritise the recovering process. The two parameters selected for control and monitoring were volume and pressure. The results in this paper show full control for the NF supervision system on the transportation process.

show abstract

Section: Related Workmentioning

confidence: 99%

Modelling Oil Pipelines Grid: Neuro-fuzzy Supervision System

Saeed¹,

Abbod²

2017

IJISA

View full text Add to dashboard Cite

show abstract

“…The ANFIS combines the advantages of fuzzy logic controllers and artificial neural network controllers, avoiding their problems on the other hand [28]- [32].…”

Section: Tuning Of Statcom Controller Gainmentioning

confidence: 99%

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

Saxena¹,

Kumar²

2015

WJET

View full text Add to dashboard Cite

This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.

show abstract

“…[1,2,3] does not focus on that why should DC motors be preferred over AC drives. Similarly, [4,5,6,7,8] have not discussed that why should induction motors be opted rather than DC motors. The significance of AC and DC motor can be determined due to their widespread applications.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis on Controlling Speed of Universal Motor by using Fuzzy and ANFIS Controller

Jilani¹,

Ahmad²,

Jilani³

2014

IJCA

View full text Add to dashboard Cite

Machines have comforted human beings in every walk of life such as scientifically, economically as well as socially. Machines consists of immotile as well certain rotating parts which combine to reduce human efforts into well-founded and pertinent work for example pulleys, blenders,, compressors etc. For every advanced technology of machines its different parameters are needed to be controlled for a competent, productive and faultless system. This paper aims on careful investigation of two advanced type of approaches for controlling speed of universal motor. These two approaches namely Fuzzy technique and ANFIS technique are employed as powerful mechanisms for varying speed based on output graph of step response versus time. A comparative evaluation is then done on the basis of two most important parameters of machines named as percentage overshoot and settling time. Comparative analysis aids in determining the best methodology to be applied in everyday applications for producing unerring, authenticated and unimpeachable systems.

show abstract

Modeling, Design and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Speed Control of Induction Motor

Cited by 48 publications

References 35 publications

Modelling Oil Pipelines Grid: Neuro-fuzzy Supervision System

Modelling Oil Pipelines Grid: Neuro-fuzzy Supervision System

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

A Comparative Analysis on Controlling Speed of Universal Motor by using Fuzzy and ANFIS Controller

Contact Info

Product

Resources

About