scholarly journals COMPARATIVE AND PERFORMANCE ANALYSIS OF INDUCTION MOTOR WITH ANN CONTROLLER

2016 ◽  
Vol 12 (9) ◽  
pp. 4371-4381
Author(s):  
Durairaj S ◽  
Y. Robinson ◽  
M. Muthuramalingam
Keyword(s):  
Induction Motor ◽  
Back Propagation ◽  
Industrial Sector ◽  
Rule Base ◽  
Back Propagation Algorithm ◽  
Huge Number ◽  
And Performance ◽  
Novel Design ◽  
Base Application ◽  
Real Time Application

A novel design of an adaptive artificial neural network technique (ANN) for controlling of the essential parameters, like as speed,  torque, flux, voltage, current, and power etc of the induction motor is presented in this paper. Induction motors are characterized by way of incredibly non-linear, complicated and time-various dynamics and inaccessibility of its states and outputs for measurements. Thus it can be considered as a challenging engineering difficulty in the industrial sector. A few of them, such as PI, fuzzy strategies, Fuzzy logic based controllers are regarded as capability candidates for such application for operating induction motor. Hence of which, the outcome of the controller is also random and high-rated results are probably not obtained. Resolution of the proper rule base application upon the drawback can be achieved by the use of an ANN controller, which becomes a built-in system of method for the manipulate purposes and yields results, which is the focus of this paper. Within the designed ANN scheme, neural community tactics are used to prefer an appropriate rule base, which is utilizing the back propagation algorithm. The simulation outcome provided on this paper is exhibit the effectiveness of the developed approach, which has acquired faster response time or settling times. Additionally, the procedure developed has got a huge number of benefits within the industrial sector will also be converted into a real time application making use of some interfacing cards.

scholarly journals Fuzzy membership functions tuning for speed controller of induction motor drive: performance improvement

2021 ◽  
Vol 23 (3) ◽  
pp. 1258
Author(s):  
Nabil Farah ◽  
Md Hairul Nizam Talib ◽  
Zulkifilie Bin Ibrahim ◽  
Qazwan Abdullah ◽  
Ömer Aydoğdu ◽  
...  
Keyword(s):  
Induction Motor ◽  
Speed Control ◽  
Peak Position ◽  
Small Error ◽  
Rule Base ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Speed Controller ◽  
Positive Side ◽  
And Performance

<p>Fuzzy <span>logic controller (FLC) has gained high interest in the field of speed control of machine drives in both academic and industrial communities. This is due to the features of FLC of handling non-linearity and variations. FLC system consists of three main elements: scaling factors (SFs), membership functions (MFs), and rule-base. Fuzzy MFs can be designed with different types and sizes. For induction motor (IM) speed control, (3x3), (5x5) and (7x7) MFs are the most used MFs sizes, and normally designed based on symmetrical distribution. However, changing the width and peak position of MFs design enhance the performance. In this paper, tuning of MFs of FLC speed control of IM drives is considered. Considering (3x3), (5x5) and (7x7) MFs sizes, the widths and peak positions of these MFs are asymmetrically distributed to improve the performance of IM drive. Based on these MFs sizes, the widths and peak positions are moved toward the origin (zero), negative and positive side that produces a controller less sensitive to the small error variations. Based on simulation and performance evaluations, improvement of 5% in settling time (Ts), 0.5% in rise time and 20% of steady-state improvement achieved with the tuned MFs compared to original </span>MFs.</p>

scholarly journals Fuzzy control of induction motor with reduced rule base

2007 ◽  
Vol 4 (2) ◽  
pp. 147-159 ◽  
Author(s):  
Pradeep Chatterjee ◽  
B.M. Karan ◽  
P.K. Sinha
Keyword(s):  
Fuzzy Control ◽  
Induction Motor ◽  
Induction Motors ◽  
Fuzzy Controller ◽  
Direct Torque Control ◽  
Torque Control ◽  
Control Technique ◽  
Rule Base ◽  
Final State ◽  
And Performance

Several methods have been proposed and published for control of induction motors. The objective of this work is to develop a fuzzy controller with reduced rule base. Using concepts of direct torque control, a fuzzy controller has been designed with a cascaded final state selector, which reduced rule base and gave birth to a new control technique of induction motor. Details and performance of the fuzzy controller has been discussed in the paper.

scholarly journals Design and Performance Evaluation of a Step-Up DC–DC Converter with Dual Loop Controllers for Two Stages Grid Connected PV Inverter

2022 ◽  
Vol 14 (2) ◽  
pp. 811
Author(s):  
Muhammad Yasir Ali Khan ◽  
Haoming Liu ◽  
Salman Habib ◽  
Danish Khan ◽  
Xiaoling Yuan
Keyword(s):  
Back Propagation ◽  
Current Control ◽  
Polynomial Method ◽  
Voltage Gain ◽  
Back Propagation Algorithm ◽  
Theoretical Concepts ◽  
Pv Inverter ◽  
Low Duty Cycle ◽  
Voltage Stress ◽  
And Performance

In this work, a non-isolated DC–DC converter is presented that combines a voltage doubler circuit and switch inductor cell with the single ended primary inductor converter to achieve a high voltage gain at a low duty cycle and with reduced component count. The converter utilizes a single switch that makes its control very simple. The voltage stress across the semiconductor components is less than the output voltage, which makes it possible to use the diodes with reduced voltage rating and a switch with low turn-on resistance. In particular, performance principle of the proposed converter along with the steady state analysis such as voltage gain, voltage stress on semiconductor components, and design of inductors and capacitors, etc., are carried out and discussed in detail. Moreover, to regulate a constant voltage at a DC-link capacitor, back propagation algorithm-based adaptive control schemes are designed. These adaptive schemes enhance the system performance by dynamically updating the control law parameters in case of PV intermittency. Furthermore, a proportional resonant controller based on Naslin polynomial method is designed for the current control loop. The method describes a systematic procedure to calculate proportional gain, resonant gain, and all the coefficients for the resonant path. Finally, the proposed system is simulated in MATLAB and Simulink software to validate the analytical and theoretical concepts along with the efficacy of the proposed model.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

scholarly journals Two-Stage Water Jet Landing Point Prediction Model for Intelligent Water Shooting Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2704
Author(s):  
Yunhan Lin ◽  
Wenlong Ji ◽  
Haowei He ◽  
Yaojie Chen
Keyword(s):  
Pitch Angle ◽  
Back Propagation ◽  
Water Jet ◽  
The State ◽  
Model Parameters ◽  
Propagation Time ◽  
Back Propagation Algorithm ◽  
Angle Error ◽  
Landing Point ◽  
Propagation Algorithm

In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fit a model that satisfies the law of jet motion in ideal conditions without interference. In the second stage, the model uses the back-propagation algorithm to update the parameters online according to the visual feedback of the landing point position. The model established by this method can dynamically eliminate the interference of external factors and realize precise on-target shooting. The simulation results show that the model can dynamically adjust the parameters according to the state relationship between the landing point and the desired target, which keeps the predicted pitch angle error within 0.1°. In the test on the actual platform, when the landing point is 0.5 m away from the position of the desired target, the model only needs 0.3 s to adjust the water jet to hit the target. Compared to the state-of-the-art method, GA-BP (genetic algorithm-back-propagation), the proposed method’s predicted pitch angle error is within 0.1 degree with 1/4 model parameters, while costing 1/7 forward propagation time and 1/200 back-propagation calculation time.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION

2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Back Propagation ◽  
Adaptive Mesh ◽  
Learning System ◽  
Back Propagation Algorithm ◽  
Adaptive Smoothing ◽  
Different Types ◽  
Optimal Values

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

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