Maximum Adhesion Control of Railway Based on Sliding Mode Control System

2011 ◽  
Vol 383-390 ◽  
pp. 5242-5249 ◽  
Author(s):  
Yun Feng Li ◽  
Xiao Yun Feng ◽  
Rui Kuo Liu
Keyword(s):  
Least Squares Method ◽  
Sliding Mode ◽  
Reference Value ◽  
Variable Structure ◽  
Recursive Least Squares ◽  
Adhesion Coefficient ◽  
Reference Velocity ◽  
Torque Transmission ◽  
Vehicle Velocity ◽  
Recursive Least Squares Method

The wheels will idle when the relative slipping speed between the wheel and rail exceeds the reference slipping speed. In order to avoid this phenomenon, the simplified model of wheel-rail traction torque transmission was established. And the adhesion coefficient and vehicle velocity are got through the disturbance observer. Then the recursive least squares method was used to forecast the slope of the adhesion-slip curve. Sliding variable structure controller was used to control the error of wheel velocity and reference velocity. From the results of simulation, this method can be effective to maintain the adhesion coefficient around the maximum. And the slipping speed approached the reference value, so the damage for wheel and rail was effectively prevented which achieved the desired effect.

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

Generating Singleton Fuzzy Models from Data with Interpretable Partition

2012 ◽  
Vol 629 ◽  
pp. 784-791 ◽  
Author(s):  
Juan Contreras
Keyword(s):  
Function Approximation ◽  
Least Squares Method ◽  
Fuzzy Model ◽  
Approximation Problem ◽  
Classification Problem ◽  
Descent Method ◽  
Recursive Least Squares ◽  
Gradient Descent Method ◽  
Recursive Least Squares Method ◽  
Triangular Membership Function

This paper presents a new methodology for obtaining singleton fuzzy model from experimental data. Each input variable is partitioned into triangular membership functions so that consecutive fuzzy sets exhibit and specific overlapping of 0.5. The recursive least squares method is employed to adjust the singleton consequences and the gradient descent method is employed to update only the modal value of each triangular membership function to preserve the overlap and reducing the number of parameters to be estimated. Applications to a function approximation problem and to a pattern classification problem are illustrated.

MEMS Gyro Random Drift Model Parameter Identification Based on Two-Stage Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 1044-1047 ◽  
Author(s):  
Zhao Hua Liu ◽  
Jia Bin Chen ◽  
Yu Liang Mao ◽  
Chun Lei Song
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Average ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Random Drift ◽  
Two Stage ◽  
Autoregressive Moving Average Model ◽  
Drift Model ◽  
Recursive Least Squares Method

Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

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