Constrained unscented Kalman filter for parameter identification of structural systems

2021 ◽  
Author(s):  
Dan Li ◽  
Yang Wang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Structural Systems

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

A Two-stage Parametric Identification of Strong Nonlinear Structural Systems with Incomplete Response Measurements

2016 ◽  
Vol 16 (04) ◽  
pp. 1640022 ◽  
Author(s):  
Lijun Liu ◽  
Ying Lei ◽  
Mingyu He
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Nonlinear Problems ◽  
Parametric Identification ◽  
Structural Systems ◽  
Two Stage ◽  
Incomplete Response ◽  
Nonlinear Structural ◽  
Structural Responses

Compared with the identification of linear structural parameters, it is more difficult to conduct parametric identification of strong nonlinear structural systems, especially when only incomplete structural responses are available. Although the extended Kalman filter (EKF) is useful for structural identification with partial measurements of structural responses and can be extended for the identification of nonlinear structures, EKF approximates nonlinear system through Taylor series expansion and is therefore not effective for the identification of strong nonlinear structural systems. Other approaches such as the unscented Kalman filter (UKF) have been proposed for the identification of strong nonlinear problems. Based on the fact that nonlinearities exist in local areas of structures, a straightforward two-stage identification approach is proposed in this paper for the identification of strong nonlinear structural parameters with incomplete response measurements. In the first stage, the locations of nonlinearities are identified based on the EKF for the identification of the equivalent linear structures. In the second stage, the UKF is utilized to identify the parameters of strong nonlinear structural systems. Therefore, the parametric identification of strong nonlinear structural parameters is simplified by the proposed approach. Several numerical examples with different nonlinear models and locations are used to validate the proposed approach.

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.

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