scholarly journals A Strain-Based Intelligent Tire to Detect Contact Patch Features for Complex Maneuvers

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1750 ◽  
Author(s):  
Mª Fernanda Mendoza-Petit ◽  
Daniel García-Pozuelo ◽  
Vicente Díaz ◽  
Oluremi Olatunbosun
Keyword(s):  
Fuzzy Logic ◽  
Contact Point ◽  
Contact Length ◽  
Fuzzy Logic System ◽  
Simulation Software ◽  
Slip Angle ◽  
Rolling Velocity ◽  
Contact Patch ◽  
Essential Components ◽  
Logic System

Tires are essential components of vehicles and are able to transmit traction and braking forces to the contact patch, contribute to directional stability, and also help to absorb shocks. If these components can provide information related to the tire–road interaction, vehicle safety can be increased. This research is focused on developing the tire as an active sensor capable to provide its functional parameters. Therefore, in this work, we studied strain-based measurements on the contact patch to develop an algorithm to compute the wheel velocity at the contact point, the effective rolling radius and the contact length on dynamic situations. These parameters directly influence the dynamics of wheel behavior which nowadays is not clearly defined. Herein, hypotheses have been assumed based on previous studies to develop the algorithm. The results expose to view an experimental test regarding influence of the tire operational condition (slip angle, vertical load, and rolling velocity) onto the computed parameters. This information is used to feed a fuzzy logic system capable of estimating the effective radius and contact length. Furthermore, a verification process has been carried out using CarSim simulation software to get the inputs for the fuzzy logic system at complex maneuvers.

Fuzzy Logic System in Quantifying Human Driving Skill for Human Adaptive Mechatronics

2015 ◽  
Vol 735 ◽  
pp. 304-310 ◽  
Author(s):  
Mohamad Hafis Izran Ishak ◽  
A.W.A. Aziz ◽  
M.F.A.M. Kasai
Keyword(s):  
Computer Simulation ◽  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Simulation Software ◽  
Membership Functions ◽  
Human Skill ◽  
Input And Output ◽  
Driving Skill ◽  
Human Skills ◽  
Logic System

Human Adaptive Mechatronic (HAM) is an enhance system for Human Machine System (HMS). Instead of one-way relationship between human and machine, HAM system provides two-way relationship between human and machine in order to assist human and to improve human skills in operating the machine. Driving a car is an example of applications where HAM system can be applied. One of the problems of HAM is to quantify the human skill for operating the machine. Therefore, this paper proposed a method to quantify human driving skill using Fuzzy Logic System (FLS). In order to get the best design of FLS for quantifying human driving skill, twelve designs of FLS were designed and tested using computer simulation software. The best design from all twelve designs is then been compared with other method of quantifying human skill for verification. Results show that the design of membership functions for input and output have big impact to the accuracy of the output.

A New Hardware Architecture for Fuzzy Logic System Acceleration

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

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

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

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

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.

scholarly journals Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7084
Author(s):  
Song Kang ◽  
Yongfeng Rong ◽  
Wusheng Chou
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Entire System ◽  
Actuator Faults ◽  
Extended State ◽  
Parameter Uncertainties ◽  
External Disturbances ◽  
Measurement Noises ◽  
Fuzzy Adaptive ◽  
Logic System

In this paper, an output-feedback fuzzy adaptive dynamic surface controller (FADSC) based on fuzzy adaptive extended state observer (FAESO) is proposed for autonomous underwater vehicle (AUV) systems in the presence of external disturbances, parameter uncertainties, measurement noises and actuator faults. The fuzzy logic system is incorporated into both the observers and controllers to improve the adaptability of the entire system. The dynamics of the AUV system is established first, considering the external disturbances and parameter uncertainties. Based on the dynamic models, the ESO, combined with a fuzzy logic system tuning the observer bandwidth, is developed to not only adaptively estimate both system states and the lumped disturbances for the controller, but also reduce the impact of measurement noises. Then, the DSC, together with fuzzy logic system tuning the time constant of the low-pass filter, is designed using estimations from the FAESO for the AUV system. The asymptotic stability of the entire system is analyzed through Lyapunov’s direct method in the time domain. Comparative simulations are implemented to verify the effectiveness and advantages of the proposed method compared with other observers and controllers considering external disturbances, parameter uncertainties and measurement noises and even the actuator faults that are not considered in the design process. The results show that the proposed method outperforms others in terms of tracking accuracy, robustness and energy consumption.

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