Combined estimation method for inertia properties of STSAT-3

2010 ◽  
Vol 24 (8) ◽  
pp. 1737-1741 ◽  
Author(s):  
Dong Hoon Kim ◽  
Sungwook Yang ◽  
Dong-Ik Cheon ◽  
Sangchul Lee ◽  
Hwa-Suk Oh
Keyword(s):  
Estimation Method ◽  
Combined Estimation ◽  
Inertia Properties

scholarly journals Multi-quadric collocation model of horizontal crustal movement

2015 ◽  
Vol 7 (4) ◽  
pp. 3359-3382
Author(s):  
G. Chen ◽  
A. M. Zeng ◽  
F. Ming ◽  
Y. F. Jing
Keyword(s):  
Velocity Field ◽  
Kernel Function ◽  
Covariance Function ◽  
Estimation Method ◽  
Crustal Movement ◽  
Movement Velocity ◽  
Euler Vector ◽  
Combined Estimation ◽  
Horizontal Crustal Movement ◽  
Fitting Model

Abstract. To establish the horizontal crustal movement velocity field of the Chinese mainland, a Hardy multi-quadric fitting model and collocation are usually used, but the kernel function, nodes, and smoothing factor are difficult to determine in the Hardy function interpolation, and in the collocation model the covariance function of the stochastic signal must be carefully constructed. In this paper, a new combined estimation method for establishing the velocity field, based on collocation and multi-quadric equation interpolation, is presented. The crustal movement estimation simultaneously takes into consideration an Euler vector as the crustal movement trend and the local distortions as the stochastic signals, and a kernel function of the multi-quadric fitting model substitutes for the covariance function of collocation. The velocities of a set of 1070 reference stations were obtained from the Crustal Movement Observation Network of China (CMONOC), and the corresponding velocity field established using the new combined estimation method. A total of 85 reference stations were used as check points, and the precision in the north and east directions was 1.25 and 0.80 mm yr−1, respectively. The result obtained by the new method corresponds with the collocation method and multi-quadric interpolation without requiring the covariance equation for the signals.

scholarly journals Multi-quadric collocation model of horizontal crustal movement

Solid Earth ◽  
2016 ◽  
Vol 7 (3) ◽  
pp. 817-825
Author(s):  
Gang Chen ◽  
Anmin Zeng ◽  
Feng Ming ◽  
Yifan Jing
Keyword(s):  
Velocity Field ◽  
Kernel Function ◽  
Covariance Function ◽  
Estimation Method ◽  
Crustal Movement ◽  
Movement Velocity ◽  
Euler Vector ◽  
Combined Estimation ◽  
Horizontal Crustal Movement ◽  
Fitting Model

Abstract. To establish the horizontal crustal movement velocity field of the Chinese mainland, a Hardy multi-quadric fitting model and collocation are usually used. However, the kernel function, nodes, and smoothing factor are difficult to determine in the Hardy function interpolation. Furthermore, the covariance function of the stochastic signal must be carefully constructed in the collocation model, which is not trivial. In this paper, a new combined estimation method for establishing the velocity field, based on collocation and multi-quadric equation interpolation, is presented. The crustal movement estimation simultaneously takes into consideration an Euler vector as the crustal movement trend and the local distortions as the stochastic signals, and a kernel function of the multi-quadric fitting model substitutes for the covariance function of collocation. The velocities of a set of 1070 reference stations were obtained from the Crustal Movement Observation Network of China, and the corresponding velocity field was established using the new combined estimation method. A total of 85 reference stations were used as checkpoints, and the precision in the north and east component was 1.25 and 0.80 mm yr−1, respectively. The result obtained by the new method corresponds with the collocation method and multi-quadric interpolation without requiring the covariance equation for the signals.

scholarly journals Rigid Body Inertia Estimation Using Extended Kalman and Savitzky-Golay Filters

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Donghoon Kim ◽  
Sungwook Yang ◽  
Sangchul Lee
Keyword(s):  
Rigid Body ◽  
Equations Of Motion ◽  
Time Derivative ◽  
Estimation Method ◽  
Space Vehicle ◽  
Space Vehicles ◽  
Sampled Data ◽  
Precise Control ◽  
Inertia Properties ◽  
Noisy Measurements

Inertia properties of rigid body such as ground, aerial, and space vehicles may be changed by several occasions, and this variation of the properties influences the control accuracy of the rigid body. For this reason, accurate inertia properties need to be obtained for precise control. An estimation process is required for both noisy gyro measurements and the time derivative of the gyro measurements. In this paper, an estimation method is proposed for having reliable estimates of inertia properties. First, the Euler equations of motion are reformulated to obtain a regressor matrix. Next, the extended Kalman filter is adopted to reduce the noise effects in gyro angular velocity measurements. Last, the inertia properties are estimated using linear least squares. To achieve reliable and accurate angular accelerations, a Savitzky-Golay filter based on an even number sampled data is utilized. Numerical examples are presented to demonstrate the performance of the proposed algorithm for the case of a space vehicle. The numerical simulation results show that the proposed algorithm provides accurate inertia property estimates in the presence of noisy measurements.

scholarly journals A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2548
Author(s):  
Angelo Bonfitto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hybrid Electric Vehicle ◽  
Estimation Method ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
State Of Health ◽  
Combined Estimation ◽  
Artificial Neural

This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system.

A Polynomial-Chaos-Based Bayesian Approach for Estimating Uncertain Parameters of Mechanical Systems

2007 ◽  
Author(s):  
Emmanuel Blanchard ◽  
Corina Sandu ◽  
Adrian Sandu
Keyword(s):  
Polynomial Chaos ◽  
Estimation Method ◽  
Maximum Likelihood Estimates ◽  
Uncertain Parameters ◽  
Bayesian Theorem ◽  
Collocation Points ◽  
Inertia Properties ◽  
Computationally Expensive ◽  
Dynamics Simulations ◽  
Polynomial Chaos Theory

In this study, a new computational approach for parameter identification is proposed based on the application of the polynomial chaos theory. The polynomial chaos method has been shown to be considerably more efficient than Monte Carlo in the simulation of systems with a small number of uncertain parameters. In the new approach presented in this paper, the maximum likelihood estimates are obtained by minimizing a cost function derived from the Bayesian theorem. Direct stochastic collocation is used as a less computationally expensive alternative to the traditional Galerkin approach to propagate the uncertainties through the system in the polynomial chaos framework. The new parameter estimation method is illustrated on a four degree-of-freedom roll plane model of a vehicle in which the vertical stiffnesses of the tires are estimated from periodic observations of the displacements and velocities across the suspensions. The results obtained with this approach are close to the actual values of the parameters even when only measurements with low sampling rates are available. The accuracy of the estimations has been shown to be sensitive to the number of terms used in the polynomial expressions and to the number of collocation points, and thus it may become computationally expensive when a very high accuracy of the results is desired. However, the noise level in the measurements affects the accuracy of the estimations as well. Therefore, it is usually not necessary to use a large number of terms in the polynomial expressions and a very large number of collocation points since the addition of extra precision eventually affects the results less than the effect of the measurement noise. Possible applications of this theory to the field of vehicle dynamics simulations include the estimation of mass, inertia properties, as well as other parameters of interest.

Coaches estimation method for the athletes (CEM)

1995 ◽  
Author(s):  
Nagykaldi Csaba ◽  
Manohar Singh Badhan
Keyword(s):  
Estimation Method
Sign in / Sign up

Export Citation Format

Share Document