A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems

Al-Dunainawi, Yousif; Abbod, Maysam F.; Jizany, Ali

doi:10.1016/j.engappai.2017.04.016

Cited by 44 publications

(22 citation statements)

References 32 publications

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“…A new MIMO ANFIS-based NARMA-L2 controller has been proposed and compared with other PI, PID, Fuzzy, GA and PSO controlled Fuzzy logic controls. The superiority of the proposed method in low tracking error and time response behaviors has been demonstrated by the simulation [17].…”

Section: Introductionmentioning

confidence: 88%

Speed Control of BLDC Using NARMA-L2 Controller in Single Link Manipulator

Çelikel

2019

Balkan Journal of Electrical and Computer Engineering

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The BLDC motors are widely used in industrial applications. The load characteristic and controller are important to obtain optimum performance in the speed control of the BLDC motor because some problems are occurred in speed control of nonlinear loads which is used the conventional control methods. The single link manipulator used in robotic applications is a nonlinear system for control algorithms. The load of motor is determined on considering the conversion ratio of the reducer box. In this study, a single link manipulator system consist of BLDC motor, reducer gearbox and robot arm is controlled by using NARMA-L2 controller as known modern control method in used the control of the nonlinear systems. The NARMA-L2 controller is based on neural network. The single link manipulator system is analyzed by using MATLAB/Simulink environment. The speed control of the BLDC motor is performed in conditions the constant and nonlinear load. The results show that the speed control of BLDC motor is performed the high performance in nonlinear loads.

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Section: Introductionmentioning

confidence: 88%

Speed Control of BLDC Using NARMA-L2 Controller in Single Link Manipulator

Çelikel

2019

Balkan Journal of Electrical and Computer Engineering

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“…The neural network-based NARMA-L2 system model requires two neural networks to be trained representing the functions, f and g, stated in (15). A multilayer neural network with enough number of neurons in the hidden layer can have diverse applications such as system identification and adaptive control [51]. Hence, the ANN can accurately mimic the non-linearity in the NARMA-L2 system model.…”

Section: Training Of Narma-l2 Modelmentioning

confidence: 99%

Performance improvement of DFIG‐based wind farms using NARMA‐L2 controlled bridge‐type flux coupling non‐superconducting fault current limiter

Islam¹,

Hasan²,

Hasan³

et al. 2020

IET gener. transm. distrib.

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Doubly-fed induction generators (DFIGs) have drawn prominent interest in the field of wind power generation, but they are vulnerable to grid faults. Grid codes mandate DFIGs to employ a sort of fault ride-through (FRT) technique during faults. Fault current limiters (FCLs) always help to augment the FRT capability of DFIGs and a non-linear controller boosts their performances. In this study, a non-linear auto-regressive moving average-L2 (NARMA-L2) controller-based bridge-type flux coupling non-superconducting FCL (BFC-NSFCL) is proposed to enhance the FRT capability of the wind farm. The authors analysed the performance of the proposed NARMA-L2-based BFC-NSFCL (NL2-BFC-NSFCL) against that of the conventionally used series dynamic braking resistor (SDBR), bridge-type FCL (BFCL), and proportional-integral (PI) controller-based BFC-NSFCL (PI-BFC-NSFCL). They tested the performance of the NL2-BFC-NSFCL through multiple temporary and permanent fault scenarios and carried out the mathematical and graphical analysis in MATLAB/Simulink platform. They found that the proposed NL2-BFC-NSFCL's performance surpasses the performances of the SDBR, the BFCL, and the PI-BFC-NSFCL. Moreover, the NL2-BFC-NSFCL has faster system recovery capability after the occurrence of any fault than other competitors.

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“…The linguistic notations are defined by sets in fuzzy and the truth-value of the linguistic expressions are defined by the membership function [21]. The universe of discourse is the domain and the range interval is [0,1], which defines the functions (i.e.…”

Section: Fuzzy Modelmentioning

confidence: 99%

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

Prasad¹,

Francis²,

Huq³

2020

IJIES

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Nowadays, several researchers are facing challenges on the prediction of diseases from the huge volume of medical databases. So, researchers are using data mining techniques like association rules, classification and clustering to address challenges. The physicians make the right decisions for successful diagnosis of various diseases by using the prediction. The existing work classifies the data to predict the certain diseases, but still it faces the difficulties due to overfitting in the training data. The main aim of this research work is to classify the Medical Data (MD) by developing the feature selection based approach in MD Classification as MDC. The irrelevant features are eliminated from the MD by using Recursive Feature Elimination (RFE) method, then ranked the features to reduce the computation cost of the proposed method. The ranked features from the RFE are given as input to the Fuzzy Neural Network (FNN) with Reinforcement Learning (RL), which is used for classification. The proposed RFE-FNN method has the accuracy of the 98.57%, 98.15% sensitivity, 98.64% specificity and 95.47% F-Measure in Heart disease dataset.

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A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems

Cited by 44 publications

References 32 publications

Speed Control of BLDC Using NARMA-L2 Controller in Single Link Manipulator

Speed Control of BLDC Using NARMA-L2 Controller in Single Link Manipulator

Performance improvement of DFIG‐based wind farms using NARMA‐L2 controlled bridge‐type flux coupling non‐superconducting fault current limiter

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

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