In this paper, a new sensorless model reference adaptive method is used for direct control of active and reactive power of the doubly fed induction generator (DFIG). In order to estimate the rotor speed, a high frequency signal injection scheme is implemented. In this study, to improve the accuracy of speed estimation, two methods are suggested. First, the coefficients of proportional-integral ...

This article proposes a novel longitudinal vehicle speed estimator for snowy roads in extreme conditions (four-wheel slip) based on low-cost wheel encoders and acceleration sensor. The tire rotation factor, η, is introduced to reduce the deviation between radius manufacturer’s marked radius. Local Vehicle Speed Estimator defined eliminate estimation error. It improves slip accuracy of four-whee...

The main purpose of this paper is to provide a methodology for discussing the fuzzy. Bayesian system reliability from the fuzzy component reliabilities, actually we discuss on the Fuzzy Bayesian system reliability assessment based on Pascal distribution, because the data sometimes cannot be measured and recorded precisely. In order to apply the Bayesian approach, the fuzzy parameters are assume...

let be a random sample from a normal distribution with unknown mean and known variance the usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. in many practical situations, is known in advance to lie in an interval, say for some in this case, the maximum likelihood estimator changes and d...

In this paper, an uncertainty and disturbance estimator (UDE)-based control is employed to ensure the finite-time tracking rejection performance for a class of Takagi–Sugeno fuzzy switched systems including additive time-varying delays, unknown uncertainties disturbances. To be precise, robust achieved by estimating disturbances with aid low-pass filter means appropriate bandwidth choice desire...

We analyze the use of clustering methods for the automatic identification of fuzzy inference models for autoregressive prediction of time series. A methodology that combines fuzzy methods and residual variance estimation techniques is followed. A nonparametric residual variance estimator is used for a priori input and model selection. A simple scheme for initializing the widths of the input mem...

In this paper, an architecture of fuzzy neural networks with Takagi-Sugeno-Kang (TSK) fuzzy rules is proposed. A novel learning algorithm [1]-[2] with self-organizing ability and fast learning rules is also presented. In the structure identification phase of our method, fuzzy IF-THEN rules are extracted with a self-constructing rule generation algorithm. In the parameter identification phase, a...

This paper addresses model-based fuzzy control. A constructive and automated method for the design of a gain-scheduling controller is presented. Based on a given Takagi–Sugeno fuzzy model of the plant, the controller is designed such that stability and prescribed performance of the closed loop are guaranteed. These properties are valid in a wide working range around an equilibrium without restr...