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This paper presents a tire–road friction coefficient estimation method based on the six-degrees-of-freedom (6-DoF) vehicle body acceleration. The key principle of the proposed method is to estimate the tire–road friction coefficient from the accelerations at the tire. A vehicle model is applied to the 6-DoF vehicle body acceleration to derive longitudinal, lateral, and normal accelerations at each...
A motion control problem of a rear-steered wheeled vehicle in consideration of the presence of uncertainties is addressed. Modeling error and additional uncertainties are taken into consideration. A sliding mode controller combining with a radial basis function neural network (RBFNN)-based estimator is proposed. The stability of the proposed control method is proven using Lyapunov stability analysis...
In the experiment, four different inter-stimulus intervals (ISIs) are utilized: 325 ms, 350 ms, 375 ms, and 400 ms. The applicability of an adaptive nonlinear principle component analysis method for extracting the P300 waves included in the EEG signals without down-sampling and averaging of the original signals was demonstrated. Back-propagation neural networks were used as the P300 classifier. After...
This paper presents a method to estimate the signal strength of the functional near infrared spectroscopy (fNIRS) as parameters of orthogonalized form of general linear model (GLM). The idea is to convert the basis function (design matrix) or explanatory variables of a GLM into orthogonal functions using the usual Gram-Schmidt orthogo-nalization procedure. The coefficients associated with the orthogonal...
To develop an effective vehicle control system, it is needed to estimate vehicle motions accurately, and the estimation of reliable road frictions is one of the most important steps to achieve this goal. In the absence of commercially available transducers to measure the friction coefficient directly, various types of estimation methods have been investigated in the past. Most models in the literature...
This paper addresses a robot pose estimation problem. The data received from the inertial sensors are converted to a diverse odometry, after that the current robot pose is estimated by utilizing the different probabilistic motion models and estimate algorithms. In addition, the experimental evaluations are performed in indoor environment and conclusions are given.
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