Impact Induced Composite Delamination: State and Parameter Identification via Unscented Kalman Filter

2008 ◽  
pp. 1251-1252
Author(s):  
Alberto Corigliano ◽  
Aldo Ghisi ◽  
Stefano Mariani
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter

Parameter identification of the dynamic Winkler soil–structure interaction model using a hybrid unscented Kalman filter–multi-objective harmony search algorithm

2020 ◽  
Vol 23 (12) ◽  
pp. 2653-2668
Author(s):  
Javier Naranjo-Pérez ◽  
Javier Fernando Jiménez-Alonso ◽  
Andrés Sáez
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Soil Structure ◽  
Optimization Problem ◽  
Unscented Kalman Filter ◽  
Harmony Search ◽  
Soil Structure Interaction ◽  
Structure Interaction ◽  
Multi Objective ◽  
Winkler Model

Soil–structure interaction is a key aspect to take into account when simulating the response of civil engineering structures subjected to dynamic actions. To this end, and due to its simplicity and ease of implementation, the dynamic Winkler model has been widely used in practical engineering applications. In this model, soil–structure interaction is simulated by means of spring–damper elements. A crucial point to guarantee the adequate performance of the approach is to accurately estimate the constitutive parameters of these elements. To this aim, this article proposes the application of a recently developed parameter identification method to address such problem. In essence, the parameter identification problem is transformed into an optimization problem, so that the parameters of the dynamic Winkler model are estimated by minimizing the relative differences between the numerical and experimental modal properties of the overall soil–structure system. A recent and efficient hybrid algorithm, based on the combination of the unscented Kalman filter and multi-objective harmony search algorithms, is satisfactorily implemented to solve the optimization problem. The performance of this proposal is then validated via its implementation in a real case-study involving an integral footbridge.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals Parameter Identification of the Acrobot Using the Unscented Kalman Filter

2005 ◽  
Vol 71 (708) ◽  
pp. 2563-2570 ◽  
Author(s):  
Nozomu ARAKI ◽  
Michito OKADA ◽  
Yasuo KONISHI ◽  
Hiroyuki ISHIGAKI
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter

Spacecraft Attitude and System Identification Using Marginal Reduced UKF Utilizing the Sun and Calibrated TAM Sensors

2012 ◽  
Vol 225 ◽  
pp. 417-422 ◽  
Author(s):  
Maryam Kiani ◽  
Seid H. Pourtakdoust
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Attitude Determination ◽  
Unscented Kalman Filter ◽  
Computational Cost ◽  
Nonlinear Estimation ◽  
Inertia Tensor ◽  
Sensor Calibration ◽  
State Equations ◽  
Calibration Parameters

This paper deals with attitude determination, parameter identification and reference sensor calibration simultaneously. A LEO satellite’s attitude, inertia tensor as well as calibration of Three-Axis-Magnetometer (TAM) are estimated during a maneuver designed to satisfy persistency of excitation condition. For this purpose, kinematic and kinetic state equations of spacecraft motion are augmented for the determination of inertia tensor and TAM calibration parameters including scale factors, misalignments and biases along three body axes. Attitude determination is a nonlinear estimation problem. Unscented Kalman Filter (UKF) as an advanced nonlinear estimation algorithm with good performance can be used to estimate satellite attitude but its computational cost is considerably larger than the widespread, low accuracy, Extended Kalman Filter (EKF). Reduced Sigma Points Filters provide good solutions and also decrease run time of UKF. However, in contrast to nonlinear problem of attitude determination, parameter identification and sensor calibration have linear dynamics. Therefore, a new Marginal UKF (MUKF) is proposed that combines the utility of Kalman Filter with Modified UKF (MMUKF). The proposed MMUKF utilizes only 14 sigma points to achieve the complete 25-dimensional state vector estimation. Additionally, a Monte Carlo simulation has demonstrated a good accuracy for concurrent estimation of attitude, inertia tensor as well as TAM calibration parameters in significantly less time with respect to sole utilization of the UKF.

scholarly journals A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures

2021 ◽  
pp. 146134842110143
Author(s):  
Mohamadreza Sheibani ◽  
Ge Ou
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Coupling Effect ◽  
Experimental Tests ◽  
Optimization Method ◽  
Bayesian Optimization ◽  
Noise Parameters ◽  
Inclusive Approach

The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy.

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