An Adaptive Complementary Kalman Filter Using Fuzzy Logic for a Hybrid Head Tracker System

2016 ◽  
Vol 65 (9) ◽  
pp. 2163-2173 ◽  
Author(s):  
Chang Ho Kang ◽  
Chan Gook Park ◽  
Jin Woo Song
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Head Tracker

scholarly journals Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 103 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
Do-Hyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Significant Risk ◽  
Assessment Method ◽  
Logic Model ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

scholarly journals Fuzzy Logic-based Adaptive Extended Kalman Filter Algorithm for GNSS Receivers

2018 ◽  
Vol 68 (6) ◽  
pp. 560
Author(s):  
Pasumarthi Babu Sreeharsha ◽  
Venkata Ratnam Devanaboyina
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite Systems ◽  
Scintillation Index ◽  
Ionospheric Scintillation ◽  
Inference System ◽  
Gnss Receivers ◽  
Ionospheric Scintillations ◽  
Adaptive Extended Kalman Filter

<p class="p1">Designing robust carrier tracking algorithms that are operable in strident environmental conditions for global navigation satellite systems (GNSS) receivers is the discern task. Major contribution in weakening the GNSS signals are ionospheric scintillations. The effect of scintillation can be known by amplitude scintillation index <em>S</em>4 and phase scintillation index sf parameters. The proposed fuzzy logic based adaptive extended Kalman filter (AEKF) method helps in modelling the signal amplitude and phase dynamically by Auto-Regressive Exogenous (ARX) analysis using Sugeno fuzzy logic inference system. The algorithm gave good performance evaluation for synthetic Cornell scintillation monitor (CSM) data and real-time strong scintillated PRN 12 L1 C/A data on October 24<span class="s1"><sup>th</sup></span>, 2012 around 21:30 h, Brazil local time collected by GNSS software navigation receiver (GSNR’x). Fuzzy logic algorithm is implemented for selecting the ARX orders based on estimated amplitude and phase ionospheric scintillation observations. Fuzzy based AEKF algorithm has the capability to mitigate ionospheric scintillations under both geomagnetic quiet and disturbed conditions.</p>

Three-Level DTC Based on Fuzzy Logic and Neural Network of Sensorless DSSM Using Extended Kalman Filter

2015 ◽  
Vol 5 (4) ◽  
pp. 453 ◽  
Author(s):  
Elakhdar Benyoussef ◽  
Abdelkader Meroufel ◽  
Said Barkat
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Direct Torque Control ◽  
Synchronous Machine ◽  
Double Star ◽  
Torque Control ◽  
Operation Conditions ◽  
Stator Flux

This paper presents a direct torque control is applied for salient-pole double star synchronous machine without mechanical speed and stator flux linkage sensors. The estimation is performed using the extended Kalman filter known by it is ability to process noisy discrete measurements. Two control approaches using fuzzy logic DTC, and neural network DTC are proposed and compared. The validity of the proposed controls scheme is verified by simulation tests of a double star synchronous machine. The stator flux, torque, and speed are determined and compared in the above techniques. Simulation results presented in this paper highlight the improvements produced by the proposed control method based on the extended Kalman filter under various operation conditions.

KALMAN FILTER AND FUZZY LOGIC ESTIMATION OF SHORT CIRCUIT CURRENT MACHINE PARAMETERS

2010 ◽  
Author(s):  
H. M. Al-Hamadi ◽  
K. M. EL-Naggar ◽  
Nader Barsoum ◽  
G. W. Weber ◽  
Pandian Vasant
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Short Circuit ◽  
Short Circuit Current
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