Fault-tolerance control method based on fuzzy logic system

Abstract This paper proposes an adaptive higher-order sliding mode (AHOSM) control method based on the adaptive fuzzy logic system for steer-by-wire (SbW) system to achieve the tracking control of the front wheels steering angle. First, an adaptive fuzzy logic system is adopted to estimate the unknown dynamics of the SbW system. Then, the AHOSM control is constructed to overcome the lumped uncertainties including unknown external perturbation and fuzzy logic system approximation error, and has the advantage of attenuating the chattering caused by the discontinuous control signal. Finally, the adaptation scheme is designed for the dynamic gain of the proposed AHOSM controller without a priori knowledge of the bounds of the uncertainties. In contrast to the existing controllers applied in the SbW system, this controller has a better control performance in practical application. By means of Lyapunov stability analysis, it is theoretically proved that the system trajectory converges to an adjustable neighborhood of the origin in finite time. Simulations and vehicle experiments are carried out to verify the effectiveness of the proposed approach.

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A New Hardware Architecture for Fuzzy Logic System Acceleration

Iraqi Journal for Electrical And Electronic Engineering ◽

10.33762/eeej.2016.118377 ◽

2016 ◽

Vol 12 (2) ◽

pp. 188-197

Author(s):

A yahoo.com ◽

Aumalhuda Gani Abood aumalhuda ◽

A comp ◽

Dr. Mohammed A. Jodha ◽

Dr. Majid A. Alwan

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic System ◽

Hardware Architecture ◽

Logic System

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Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

Jurnal ULTIMATICS ◽

10.31937/ti.v3i2.299 ◽

2011 ◽

Vol 3 (2) ◽

pp. 11-15

Author(s):

Seng Hansun

Keyword(s):

Time Series ◽

Fuzzy Logic ◽

Time Series Analysis ◽

Fuzzy Sets ◽

Fuzzy System ◽

Time Series Data ◽

Fuzzy Logic System ◽

Series Data ◽

Series Analysis ◽

Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

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A New Interval Type-2 Fuzzy Logic System Under Dynamic Environment: Application to Financial Investment

SSRN Electronic Journal ◽

10.2139/ssrn.3532096 ◽

2020 ◽

Author(s):

Akihiko Takahashi ◽

Soichiro Takahashi

Keyword(s):

Fuzzy Logic ◽

Dynamic Environment ◽

Fuzzy Logic System ◽

Financial Investment ◽

Interval Type ◽

Logic System

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IMPROVING EIGENSPACE-BASED FUZZY LOGIC SYSTEM USING A LINEAR INTERPOLATION SCHEME FOR SPEECH PATTERN RECOGNITION

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2013-0049 ◽

2013 ◽

Vol 37 (3) ◽

pp. 611-620

Author(s):

Ing-Jr Ding ◽

Chih-Ta Yen

Keyword(s):

Pattern Recognition ◽

Fuzzy Logic ◽

Recognition Performance ◽

Linear Interpolation ◽

Fuzzy Logic System ◽

Interpolation Scheme ◽

Speech Pattern ◽

Eigen Value ◽

Target Speaker ◽

Logic System

The Eigen-FLS approach using an eigenspace-based scheme for fast fuzzy logic system (FLS) establishments has been attempted successfully in speech pattern recognition. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace is scarce. To tackle this issue, this paper proposes two improved-versioned Eigen-FLS methods, incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS, both of which use a linear interpolation scheme for properly adjusting the target speaker’s Eigen-FLS model derived from an FLS eigenspace. Developed incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS are superior to conventional Eigen-FLS especially in the situation of insufficient data from the target speaker.

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