Application of adaptive identification methods for refining parameters of radiation pressure models

2020 ◽  
pp. 35-45
Author(s):  
Оксана Сергеевна Черникова ◽  
Александр Сергеевич Толстиков ◽  
Юлия Сергеевна Четвертакова
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Unscented Kalman Filter ◽  
Parametric Identification ◽  
Discrete Systems ◽  
Likelihood Method ◽  
Navigation Satellite ◽  
Unknown Parameters ◽  
Satellite Trajectory

Представлены две адаптивные модификации сигма-точечного фильтра Калмана с рекуррентным оцениванием ковариационных матриц шумов системы и измерений, на основе которых выполняется процедура параметрической идентификации нелинейных непрерывно-дискретных систем. Применение процедуры адаптивной параметрической идентификации позволило вычислить с достаточной точностью оценки параметров нескольких моделей радиационного давления солнечного излучения. Полученные результаты повысили качество прогнозирования траектории движения навигационного спутника Purpose. The paper considers the problem of estimation of unknown parameters for various models of solar radiation based on adaptive modifications of the unscented Kalman filter. The estimations of the obtained parameters are used both in solar radiation models and in construction of trajectory of a navigation satellite. Methodology. To solve the problem of parametric identification of stochastic nonlinear continuous-discrete systems, several adaptive modifications of the unscented Kalman filter are considered. The algorithms assume recurrent estimation of covariance matrices of system noise and measurements. The maximum likelihood method is used for parametric identification of stochastic nonlinear continuous-discrete systems. Adaptive modifications of the unscented Kalman filter are used in the construction of the identification criterion. Estimates of unknown parameters of various solar radiation models are found for the movement for the navigation satellite model as an example. The satellite orbital movement forecast is made. Finding and value. The application of the adaptive parametric identification procedure allows calculating the estimates for the parameters of several models of the solar radiation with sufficient accuracy. The obtained results lead to significant improvement of quality of the prediction for satellite trajectory

scholarly journals Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

2013 ◽  
Author(s):  
Mehdi Maasoumy ◽  
Barzin Moridian ◽  
Meysam Razmara ◽  
Mahdi Shahbakhti ◽  
Alberto Sangiovanni-Vincentelli
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Convective Heat Transfer Coefficient ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Modeling Framework ◽  
Parameter Adaptation ◽  
Building Model ◽  
Model Based ◽  
On Line

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

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.

scholarly journals Effects of Environment Model Parametrization on Photogrametry Reconstruction

MENDEL ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 151-158
Author(s):  
Jan Klecka
Keyword(s):  
Kalman Filter ◽  
Maximum Likelihood ◽  
Extended Kalman Filter ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Camera Model ◽  
Experimental Part ◽  
Environment Model ◽  
Mathematical Derivation

This paper is aimed at a description of effects which have assumptions of specific environment structure on quality of recurrently conducted photogrammetry reconstruction. The theoretical part covers the description of three different assumptions of environment structure and mathematical derivation of two suitable recurrent estimators: one based on Extended Kalman filter and the second one based on Maximum likelihood method. The experimental part is introducing simple virtual environment which is observed by linear camera model and then reconstructed using predefined algorithms and assumptions.

Parameter estimation of slow potassium dynamics in a neuron model for seizure-like activity via adaptive lag synchronization and unscented Kalman filter

2019 ◽  
Vol 33 (15) ◽  
pp. 1950159
Author(s):  
Chunxiao Han ◽  
Yaru Yang ◽  
Tingting Yang ◽  
Yingmei Qin ◽  
Yanqiu Che
Keyword(s):  
Kalman Filter ◽  
Neuron Model ◽  
Unscented Kalman Filter ◽  
Unknown Parameters ◽  
Lag Synchronization ◽  
Multiple Parameters ◽  
Barbalat’S Lemma ◽  
System States ◽  
Two Parameters ◽  
New Treatments

We introduce a method that combines the unscented Kalman filter (UKF) and the adaptive lag synchronization (ALS) to estimate the unknown parameters of a neuron model with seizure-like activity using only the heavily noise-corrupted time series of membrane potentials. Although both UKF and ALS are able to estimate the parameters, UKF performs worse when the number of unknown parameters increases, while ALS requires system states that cannot be measured in practice. Therefore, we incorporate UKF as an observer of the unmeasured states into ALS method to estimate multiple parameters. The effectiveness of the combined method is guaranteed by Lyapunov stability theorem and Barbalat’s lemma in theory. Numerical simulations demonstrate that, when two parameters are estimated simultaneously, the combined approach has better performance and higher accuracy than only using UKF or ALS method. This exploration of the proposed approach may play an important role in studying new treatments in seizure control.

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