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Journal articles on the topic 'State and parameter estimations'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Andersson, Niklas, Per-Ola Larsson, Johan Åkesson, Niclas Carlsson, Staffan Skålén, and Bernt Nilsson. "Parameter Selection in the Parameter Estimation of Grade Transitions in a Polyethylene Plant." Studies in Engineering and Technology 3, no. 1 (2015): 1. http://dx.doi.org/10.11114/set.v3i1.887.

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A polyethylene plant at Borealis AB is modelled in the Modelica language and considered for parameter estimations at grade transitions. Parameters have been estimated for both the steady-state and the dynamic case using the JModelica.org platform, which offers tools for steady-state parameter estimation and supports simulation with parameter sensitivies. The model contains 31 candidate parameters, giving a huge amount of possible parameter combinations. The best parameter sets have been chosen using a parameter-selection algorithm that identified parameter sets with poor numerical properties.

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Jamal, Alaa, and Raphael Linker. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model." Agriculture 10, no. 12 (2020): 606. http://dx.doi.org/10.3390/agriculture10120606.

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Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for

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Wang, Xiaoyu, Te Chen, Renzhong Wang, Jiankang Lu, and Guowei Dou. "Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms." Sensors 25, no. 13 (2025): 3927. https://doi.org/10.3390/s25133927.

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This paper reviews research on vehicle driving state estimation research. Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the perspective of the estimation objects, including vehicle attitude and driving state estimations, chassis component key dynamic parameter estimations, and vehicle driving environment state estimations; (2) from the perspective of vehicle characteristics, including vehicle dynamics coupling characteristi

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Jung, Youngsun, Ming Xue, and Guifu Zhang. "Simultaneous Estimation of Microphysical Parameters and the Atmospheric State Using Simulated Polarimetric Radar Data and an Ensemble Kalman Filter in the Presence of an Observation Operator Error." Monthly Weather Review 138, no. 2 (2010): 539–62. http://dx.doi.org/10.1175/2009mwr2748.1.

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Abstract The impacts of polarimetric radar data on the estimation of uncertain microphysical parameters are investigated through observing system simulation experiments when the effects of uncertain parameters on the observation operators are also considered. Five fundamental microphysical parameters (i.e., the intercept parameters of rain, snow, and hail and the bulk densities of snow and hail) are estimated individually or collectively using the ensemble square root Kalman filter. The differential reflectivity ZDR, specific differential phase KDP, and radar reflectivity at horizontal polariz

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Wang, Huapeng, Nan Jiang, Ting Liu, and Yangyang Cao. "ADAPTIVE STABLE CONTROL OF MANIPULATOR SYSTEM BASED ON IMMERSION AND INVARIANCE." Mathematical Modelling and Analysis 23, no. 3 (2018): 379–89. http://dx.doi.org/10.3846/mma.2018.023.

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This work focused on the manipulator system containing uncertainties, and proposes an immersion and invariance (I&amp;I) control strategy, in order to avoid the damage on the mechanical and the operation object caused by parameter uncertainty. A stable target system with lower dimension than the manipulator system was chosen to design the control law and estimation laws of uncertain parameters. Then finding an invariant and attractive manifold in state space with internal dynamics a copy of the desired closed-loop dynamics. Finally, design a control law that can steer the state of the syst

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Astharini, Dwi, Muhamad Asvial, and Dadang Gunawan. "Estimation with Angular Parameters on Channel of Visible Light Communication." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 2 (2022): 142–51. http://dx.doi.org/10.37936/ecticit.2022162.245930.

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Performance of visible light communication (VLC) depends highly on channel conditions, which are sensitive to changes in user position. This paper presents estimation schemes of VLC parameters using a Kalman filter (KF), based on angular parameters of user position. The angular dynamic model is established so that the estimation process is directly in accordance with the Lambertian model of VLC channel. The use of angular model also gave way to use two parameters to describe a three-dimensional position. Estimations based on angular position are formulated, that is the KF estimation of positio

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Obiri, Sandra A., Bernard T. Agyeman, Sarupa Debnath, Siyu Liu, and Jinfeng Liu. "Sensor Selection and State Estimation of Continuous mAb Production Processes." Mathematics 11, no. 18 (2023): 3860. http://dx.doi.org/10.3390/math11183860.

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The production of monoclonal antibodies (mAbs) plays a pivotal role in therapeutic treatments, and optimizing their production is crucial for minimizing costs and improving their accessibility to patients. One way of improving the production process is to improve model accuracy through the correct estimation of its states and parameters. The contributions of this paper lie in the provision of guidelines for sensor selection in the upstream production process of mAbs to enhance the accuracy of state estimation. Furthermore, this paper applies an effective variable selection technique for simult

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Li, Zhengbin, Lijun Ma, and Yongqiang Wang. "Parameter estimation for nonlinear sandwich system using instantaneous performance principle." PLOS ONE 17, no. 12 (2022): e0271160. http://dx.doi.org/10.1371/journal.pone.0271160.

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The vast majority of reports mainly focus on the steady-state performance of parameter estimation. Few findings are reported for the instantaneous performance of parameter estimation because the instantaneous performance is difficult to quantify by using the design algorithm, for example, in the initial stage of parameter estimation, the error of parameter estimation varies in a specific region on the basis of the user’s request. With that in mind, we design an identification algorithm to address the transient performance of the parameter estimations. In this study, the parameter estimation of

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Nie, S., J. Zhu, and Y. Luo. "Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: idealized twin experiments." Hydrology and Earth System Sciences Discussions 8, no. 1 (2011): 1433–68. http://dx.doi.org/10.5194/hessd-8-1433-2011.

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Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, initial soil moisture condition and atmospheric forcing. A physically-based land surface model is used for this purpose. Using a series of idealized twin experiments, model generated near-surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil mois

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Hsieh, Hsien-Yi, Jingyu Ning, Yi-Ru Chen, et al. "Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography." Symmetry 14, no. 5 (2022): 874. http://dx.doi.org/10.3390/sym14050874.

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With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high pre

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Hsieh, Hsien-Yi, Jingyu Ning, Yi-Ru Chen, et al. "Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography." Symmetry 14, no. 5 (2022): 874. http://dx.doi.org/10.3390/sym14050874.

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With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high pre

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Madankan, Reza, Puneet Singla, Tarunraj Singh, and Peter D. Scott. "Polynomial-Chaos-Based Bayesian Approach for State and Parameter Estimations." Journal of Guidance, Control, and Dynamics 36, no. 4 (2013): 1058–74. http://dx.doi.org/10.2514/1.58377.

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13

Soroush, Masoud. "State and parameter estimations and their applications in process control." Computers & Chemical Engineering 23, no. 2 (1998): 229–45. http://dx.doi.org/10.1016/s0098-1354(98)00263-4.

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14

Gao, Caiwen, Zhiqiang Zhang, and Baoliang Liu. "Uncertain Logistic Population Model with Allee Effect." Scientific Insights and Discoveries Review 6 (October 14, 2024): 1–10. http://dx.doi.org/10.59782/sidr.v6i1.170.

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By considering the influence of various uncertain noises, we established an uncertain Logistic population model with Allee effect, which was characterized by an uncertain differential equation. Firstly, we obtained the solution of the model, and discussed the stability of the equilibrium state. Secondly, the unknown parameters in the model were estimated by using the generalized moment estimation method under the framework of uncertainty theory. Finally, the parameter estimations of the model and the properties of the solution were explained by an example analysis.

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Ma, Kezhao, Jia Kong, Yihan Wang, and Xiao-Ming Lu. "Review of the Applications of Kalman Filtering in Quantum Systems." Symmetry 14, no. 12 (2022): 2478. http://dx.doi.org/10.3390/sym14122478.

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State variable and parameter estimations are important for signal sensing and feedback control in both traditional engineering systems and quantum systems. The Kalman filter, which is one of the most popular signal recovery techniques in classical systems for decades, has now been connected to the stochastic master equations of linear quantum mechanical systems. Various studies have invested effort on mapping the state evolution of a quantum system into a set of classical filtering equations. However, establishing proper evolution models with symmetry to classical filter equation for quantum s

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16

Al-Mashhadani, Mohammad Abdulrahman. "Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty." Measurement and Control 52, no. 9-10 (2019): 1264–71. http://dx.doi.org/10.1177/0020294019866860.

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In the past decade, many approaches that attempted to solve the problem of optimal control and parameter estimation of an unmanned aerial vehicle with a priori uncertain parameters simply implied two ways to solve such problem. First, by the formation of optimal control based on a refined mathematical model of the unmanned aerial vehicle, and second, by using the estimation and identification methods of the model parameter of the unmanned aerial vehicle based on measured data from flight tests. However, the identification of the dynamic parameters of the unmanned aerial vehicle is a complicate

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17

Aalto, Atte. "Iterative observer-based state and parameter estimation for linear systems." ESAIM: Control, Optimisation and Calculus of Variations 24, no. 1 (2018): 265–88. http://dx.doi.org/10.1051/cocv/2017005.

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We propose an iterative method for joint state and parameter estimation using measurements on a time interval [0, T] for systems that are backward output stabilizable. Since this time interval is fixed, errors in initial state may have a big impact on the parameter estimate. We propose to use the back and forth nudging (BFN) method for estimating the system’s initial state and a Gauss–Newton step between BFN iterations for estimating the system parameters. Taking advantage of results on the optimality of the BFN method, we show that for systems with skew-adjoint generators, the initial state a

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18

Sanz-Gorrachategui, Iván, Pablo Pastor-Flores, Antonio Bono-Nuez, Cora Ferrer-Sánchez, Alejandro Guillén-Asensio, and Carlos Bernal-Ruiz. "Lithium-Ion Battery Parameter Identification via Extremum Seeking Considering Aging and Degradation." Energies 14, no. 22 (2021): 7496. http://dx.doi.org/10.3390/en14227496.

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Battery parameters such as State of Charge (SoC) and State of Health (SoH) are key to modern applications; thus, there is interest in developing robust algorithms for estimating them. Most of the techniques explored to this end rely on a battery model. As batteries age, their behavior starts differing from the models, so it is vital to update such models in order to be able to track battery behavior after some time in application. This paper presents a method for performing online battery parameter tracking by using the Extremum Seeking (ES) algorithm. This algorithm fits voltage waveforms by

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19

Zhang, Yakun, Andrea Vacca, Guofang Gong, and Huayong Yang. "Quantitative Fault Diagnostics of Hydraulic Cylinder Using Particle Filter." Machines 11, no. 11 (2023): 1019. http://dx.doi.org/10.3390/machines11111019.

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Condition-based hydraulic cylinder maintenance necessitates quantitative fault diagnostics. However, existing methods are characterized by either qualitative or limited quantitative capabilities. In this paper, a quantitative fault diagnostic method using a particle filter for hydraulic cylinders is proposed. The problem of quantitative fault diagnostics is formally formulated in a stochastic framework to assess the health/fault state, and an architecture based on joint state-parameter estimation is proposed. Through the establishment and analysis of a nonlinear dynamic model of the hydraulic

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20

SUNAHARA, Yoshifumi, Shin'Ichi AIHARA, Masaaki ISHIKAWA, and Junji KANEKO. "On the State Estimations for Stochastic Distributed Parameter Systems with Saturation." Transactions of the Society of Instrument and Control Engineers 21, no. 1 (1985): 7–12. http://dx.doi.org/10.9746/sicetr1965.21.7.

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21

Jacquez, J. A., and T. Perry. "Parameter estimation: local identifiability of parameters." American Journal of Physiology-Endocrinology and Metabolism 258, no. 4 (1990): E727—E736. http://dx.doi.org/10.1152/ajpendo.1990.258.4.e727.

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For biological systems one often cannot set up experiments to measure all of the state variables. If only a subset of the state variables can be measured, it is possible that some of the system parameters cannot influence the measured state variables or that they do so in combinations that do not define the parameters' effects separately. Such parameters are unidentifiable and are in theory unestimable. Given a model of the system, linear or nonlinear, and initial estimates of the values of all parameters, we exhibit a simple theory and describe a program for checking the local identifiability

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22

Gu, Junhua, and Jingying Wang. "Direct parameter inference from global EoR signal with Bayesian statistics." Monthly Notices of the Royal Astronomical Society 492, no. 3 (2020): 4080–96. http://dx.doi.org/10.1093/mnras/staa052.

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ABSTRACT In the observation of sky-averaged $\mathrm{H\, \small{I}}$ signal from Epoch of Reionization (EoR), model parameter inference can be a computation-intensive work, which makes it hard to perform a direct one-stage model parameter inference by using Markov Chain Monte Carlo (MCMC) sampling method in Bayesian framework. Instead, a two-stage inference is usually used, i.e. the parameters of some characteristic points on the EoR spectrum model are first estimated, which are then used as the input to estimate physical model parameters further. However, some previous works had noticed that

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23

Ait-El-Fquih, Boujemaa, Mohamad El Gharamti, and Ibrahim Hoteit. "A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology." Hydrology and Earth System Sciences 20, no. 8 (2016): 3289–307. http://dx.doi.org/10.5194/hess-20-3289-2016.

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Abstract. Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate fil

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Zhu, Tianye, Zhihan Shi, Tianyang Zhang, and Guangming Zhang. "Estimation of lithium battery state of charge using PSO-AEKF algorithm." Journal of Physics: Conference Series 2835, no. 1 (2024): 012026. http://dx.doi.org/10.1088/1742-6596/2835/1/012026.

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Abstract For the construction of a second-order model, it is necessary to accurately identify five key parameters: R 0, Rp , Cp , Rp , and Rd . Traditional offline parameter identification methods rely solely on fitting the curve during the quiescent period after discharge to determine these parameters. This paper employs the Particle Swarm Optimization (PSO) algorithm combined with a second-order RC discrete model to fit the operating curve, thereby enhancing the model’s accuracy. In subsequent estimations, an adaptive Kalman filter is introduced to compare these two sets of parameters.

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Chen, Yan, Junli Meng, Shunyang Ming, Gengxin Tong, and Ziyi Qi. "Improved Deep Extreme Learning Machine for State of Health Estimation of Lithium-Ion Battery." International Journal of Energy Research 2024 (May 18, 2024): 1–22. http://dx.doi.org/10.1155/2024/8892634.

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Prediction of state of health (SOH), a crucial aspect of battery management systems, necessitates accurate and reliable estimations for lithium-ion batteries. However, achieving high-precision SOH estimation using the deep extreme learning machine (DELM) in complex environments is challenging due to the instability caused by its random key parameters. To address this, we propose a novel approach that combines the improved bald eagle search (IBES) algorithm with DELM. By utilizing the IBES algorithm, we can extract highly relevant health indicators from the battery’s parameter curve during char

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David, I. J., S. Mathew, and J. Y. Falgore. "New Sine Inverted Exponential Distribution: Properties, Simulation and Application." European Journal of Statistics 4 (April 1, 2024): 5. http://dx.doi.org/10.28924/ada/stat.4.5.

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The New Sine Inverted Exponential Distribution, a new distribution model with just one parameter, is suggested in this study. The suggested model has several statistical qualities and reliability properties that have been constructed and explored. The MLE estimations of the parameters were determined using R's adequacy model package. To calculate the bias of the model parameter and the root mean square error, a simulation study was done. The simulation study revealed that the proposed model is well-behaved. The findings also showed that the suggested model outperforms the current listed models

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Santitissadeekorn, Naratip, and Christopher Jones. "Two-Stage Filtering for Joint State-Parameter Estimation." Monthly Weather Review 143, no. 6 (2015): 2028–42. http://dx.doi.org/10.1175/mwr-d-14-00176.1.

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Abstract This paper presents an approach for the simultaneous estimation of the state and unknown parameters in a sequential data assimilation framework. The state augmentation technique, in which the state vector is augmented by the model parameters, has been investigated in many previous studies and some success with this technique has been reported in the case where model parameters are additive. However, many geophysical or climate models contain nonadditive parameters such as those arising from physical parameterization of subgrid-scale processes, in which case the state augmentation tech

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Nguyen, Nhat, Zexiang Liu, and Xiaofan Cui. "Accurate Co-Estimation Methods for Second-Life Battery Management Systems (BMS2): Integrating State and Parameter Estimations." IFAC-PapersOnLine 58, no. 28 (2024): 804–9. https://doi.org/10.1016/j.ifacol.2025.01.073.

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Kondrashov, Dmitri, Chaojiao Sun, and Michael Ghil. "Data Assimilation for a Coupled Ocean–Atmosphere Model. Part II: Parameter Estimation." Monthly Weather Review 136, no. 12 (2008): 5062–76. http://dx.doi.org/10.1175/2008mwr2544.1.

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Abstract The parameter estimation problem for the coupled ocean–atmosphere system in the tropical Pacific Ocean is investigated using an advanced sequential estimator [i.e., the extended Kalman filter (EKF)]. The intermediate coupled model (ICM) used in this paper consists of a prognostic upper-ocean model and a diagnostic atmospheric model. Model errors arise from the uncertainty in atmospheric wind stress. First, the state and parameters are estimated in an identical-twin framework, based on incomplete and inaccurate observations of the model state. Two parameters are estimated by including

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Shuts, Simon A., Kirill M. Fridman, Boris S. Shuts, Andrey N. Bootilski, and Ellina I. Peshkova. "3D X-ray based Method for Diagnostics of Spinal Deformities." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 162–73. http://dx.doi.org/10.47059/revistageintec.v11i2.1651.

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Research Funding: The “OrthoLine” research and production company has provided funding for the research study. Conflict of interest: The authors have no conflicts of interest to declare. Research Objective: to revise the “arc angle” parameter for estimating the value of spinal curvature and, if necessary, to search for another parameter or to develop another method for mathematically correct estimation of spinal deformity value. Materials and methods. The critical analysis has been performed both theoretically and experimentally. It has been demonstrated that the angle parameter is incorrect,

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Ma, Ruizi. "Adaptive Tolerant State Estimation under Model Uncertainty in Power Systems." Energies 14, no. 8 (2021): 2111. http://dx.doi.org/10.3390/en14082111.

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In this paper an adaptive tolerant estimator using singular value decomposition is proposed for a distribution network under model uncertainty in power systems. The adaptive tolerant estimator was designed with adjusted parameters and adjusted weights to overcome the limitations of model uncertainty. The estimator that reduces the measurement errors is adaptive to fast parameter changes in complicated environments. The singular value decomposition method was combined into the state estimator, which extended the over-determined cases to under-determined cases under model uncertainty. The perfor

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Ruchi, Sangeetika, and Svetlana Dubinkina. "Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling." Nonlinear Processes in Geophysics 25, no. 4 (2018): 731–46. http://dx.doi.org/10.5194/npg-25-731-2018.

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Abstract. Over the years data assimilation methods have been developed to obtain estimations of uncertain model parameters by taking into account a few observations of a model state. The most reliable Markov chain Monte Carlo (MCMC) methods are computationally expensive. Sequential ensemble methods such as ensemble Kalman filters and particle filters provide a favorable alternative. However, ensemble Kalman filter has an assumption of Gaussianity. Ensemble transform particle filter does not have this assumption and has proven to be highly beneficial for an initial condition estimation and a sm

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Courts, Jarrad, Johannes Hendriks, Adrian Wills, Thomas B. Schön, and Brett Ninness. "Variational State and Parameter Estimation." IFAC-PapersOnLine 54, no. 7 (2021): 732–37. http://dx.doi.org/10.1016/j.ifacol.2021.08.448.

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Abarbanel, Henry D. I., Daniel R. Creveling, Reza Farsian, and Mark Kostuk. "Dynamical State and Parameter Estimation." SIAM Journal on Applied Dynamical Systems 8, no. 4 (2009): 1341–81. http://dx.doi.org/10.1137/090749761.

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Neupert, Steven, and Julia Kowal. "Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes." Batteries 9, no. 7 (2023): 364. http://dx.doi.org/10.3390/batteries9070364.

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State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The p

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Alvarado-Méndez, Pedro Eusebio, Carlos M. Astorga-Zaragoza, Gloria L. Osorio-Gordillo, Adriana Aguilera-González, Rodolfo Vargas-Méndez, and Juan Reyes-Reyes. "H∞ State and Parameter Estimation for Lipschitz Nonlinear Systems." Mathematical and Computational Applications 29, no. 4 (2024): 51. http://dx.doi.org/10.3390/mca29040051.

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A H∞ robust adaptive nonlinear observer for state and parameter estimation of a class of Lipschitz nonlinear systems with disturbances is presented in this work. The objective is to estimate parameters and monitor the performance of nonlinear processes with model uncertainties. The behavior of the observer in the presence of disturbances is analyzed using Lyapunov stability theory and by considering an H∞ performance criterion. Numerical simulations were carried out to demonstrate the applicability of this observer for a semi-active car suspension. The adaptive observer performed well in estim

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Alshawabkeh, Ashraf, Mustafa Matar, and Fayha Almutairy. "Parameters Identification for Lithium-Ion Battery Models Using the Levenberg–Marquardt Algorithm." World Electric Vehicle Journal 15, no. 9 (2024): 406. http://dx.doi.org/10.3390/wevj15090406.

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The increasing adoption of batteries in a variety of applications has highlighted the necessity of accurate parameter identification and effective modeling, especially for lithium-ion batteries, which are preferred due to their high power and energy densities. This paper proposes a comprehensive framework using the Levenberg–Marquardt algorithm (LMA) for validating and identifying lithium-ion battery model parameters to improve the accuracy of state of charge (SOC) estimations, using only discharging measurements in the N-order Thevenin equivalent circuit model, thereby increasing computationa

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Dumedah, Gift, and Jeffrey P. Walker. "Evaluation of Model Parameter Convergence when Using Data Assimilation for Soil Moisture Estimation." Journal of Hydrometeorology 15, no. 1 (2014): 359–75. http://dx.doi.org/10.1175/jhm-d-12-0175.1.

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Abstract Data assimilation (DA) methods are commonly used for finding a compromise between imperfect observations and uncertain model predictions. The estimation of model states and parameters has been widely recognized, but the convergence of estimated parameters has not been thoroughly investigated. The distribution of model state and parameter values is closely linked to convergence, which in turn impacts the ultimate estimation accuracy of DA methods. This demonstration study examines the robustness and convergence of model parameters for the ensemble Kalman filter (EnKF) and the evolution

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Özcan, Seyit Emre, and Hayri Arabaci. "Calculation vs. estimation: Impact of internal resistance determination on SOC estimation accuracy in lithium-ion batteries." Energy Storage and Conversion 3, no. 2 (2025): 3249. https://doi.org/10.59400/esc3249.

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The electrical equivalent circuit model (ECM) is widely employed for state of charge (SOC) estimation in lithium-ion batteries. Among ECM-based approaches, the Thevenin equivalent circuit model (TECM) is particularly favored due to its computational efficiency and ease of parameter identification. TECM can be implemented in various configurations, with 1RC, 2RC, and 3RC structures being the most common. In these configurations, each RC unit consists of a resistor (R) and a capacitor (C) connected in parallel and incorporated into the circuit branch in series. As the number of RC branches incre

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Nie, S., J. Zhu, and Y. Luo. "Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments." Hydrology and Earth System Sciences 15, no. 8 (2011): 2437–57. http://dx.doi.org/10.5194/hess-15-2437-2011.

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Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilat

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Banerjee, Arundhuti, Ylona van Dinther, and Femke C. Vossepoel. "On parameter bias in earthquake sequence models using data assimilation." Nonlinear Processes in Geophysics 30, no. 2 (2023): 101–15. http://dx.doi.org/10.5194/npg-30-101-2023.

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Abstract. The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios given uncertainties in both models and data. We investigate whether data assimilation can estimate current and future fault states, i.e., slip rate and shear stress, in the presence of a bias in the friction parameter. We perform state estimation as well as combined state-parameter estimation using a sequential-importance resampling particle filter in a zero-dimensional (0D) generalization of the Burridge–Knopoff spring–block model with rate-and-s

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Zhang, Xiao, Feng Ding, Ling Xu, Ahmed Alsaedi, and Tasawar Hayat. "A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise." Mathematics 7, no. 4 (2019): 356. http://dx.doi.org/10.3390/math7040356.

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This paper is concerned with the joint state and parameter estimation methods for a bilinear system in the state space form, which is disturbed by additive noise. In order to overcome the difficulty that the model contains the product term of the system input and states, we make use of the hierarchical identification principle to present new methods for estimating the system parameters and states interactively. The unknown states are first estimated via a bilinear state estimator on the basis of the Kalman filtering algorithm. Then, a state estimator-based recursive generalized least squares (

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Chen, Donghua, Ya Zhang, Cheng-Lin Liu, and Yangyang Chen. "Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks." Discrete Dynamics in Nature and Society 2018 (December 2, 2018): 1–11. http://dx.doi.org/10.1155/2018/7954263.

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This paper investigates the distributed filtering for discrete-time-invariant systems in sensor networks where each sensor’s measuring system may not be observable, and each sensor can just obtain partial system parameters with unknown coefficients which are modeled by Gaussian white noises. A fully distributed robust Kalman filtering algorithm consisting of two parts is proposed. One is a consensus Kalman filter to estimate the system parameters. It is proved that the mean square estimation errors for the system parameters converge to zero if and only if, for any one system parameter, its acc

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Bernard, Olivier, Olivier Alata, and Marc Francaux. "On the modeling of breath-by-breath oxygen uptake kinetics at the onset of high-intensity exercises: simulated annealing vs. GRG2 method." Journal of Applied Physiology 100, no. 3 (2006): 1049–58. http://dx.doi.org/10.1152/japplphysiol.00712.2005.

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Modeling in the time domain, the non-steady-state O2 uptake on-kinetics of high-intensity exercises with empirical models is commonly performed with gradient-descent-based methods. However, these procedures may impair the confidence of the parameter estimation when the modeling functions are not continuously differentiable and when the estimation corresponds to an ill-posed problem. To cope with these problems, an implementation of simulated annealing (SA) methods was compared with the GRG2 algorithm (a gradient-descent method known for its robustness). Forty simulated V̇o2 on-responses were g

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Appiah, E. A., and G. S. Ladde. "Linear Hybrid Deterministic Dynamic Modeling for Time-to-Event Processes: State and Parameter Estimations." International Journal of Statistics and Probability 5, no. 6 (2016): 32. http://dx.doi.org/10.5539/ijsp.v5n6p32.

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In this work, we initiate an innovative alternative modeling approach for time-to-event dynamic processes. The proposed approach is composed of the following basic components: (1) development of continuous-time state of dynamic process, (2) introduction of discrete-time dynamic intervention process, (3) formulation of continuous and discrete-time interconnected dynamic system, (4) utilizing Euler-type discretized schemes, and (5) introduction of conceptual and computational state and parameter estimation procedures. The presented approach is motivated by state and parameter estimation of time-

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Pereira Álvarez, Pablo Pereira, Pierre Kerfriden, David Ryckelynck, and Vincent Robin. "Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning." Mathematics 9, no. 18 (2021): 2263. http://dx.doi.org/10.3390/math9182263.

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Welding operations may be subjected to different types of defects when the process is not properly controlled and most defect detection is done a posteriori. The mechanical variables that are at the origin of these imperfections are often not observable in situ. We propose an offline/online data assimilation approach that allows for joint parameter and state estimations based on local probabilistic surrogate models and thermal imaging in real-time. Offline, the surrogate models are built from a high-fidelity thermomechanical Finite Element parametric study of the weld. The online estimations a

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Rsetam, Kamal, Yusai Zheng, Zhenwei Cao, and Zhihong Man. "Adaptive Active Disturbance Rejection Control for Vehicle Steer-by-Wire under Communication Time Delays." Applied System Innovation 7, no. 2 (2024): 22. http://dx.doi.org/10.3390/asi7020022.

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In this paper, an adaptive active disturbance rejection control is newly designed for precise angular steering position tracking of the uncertain and nonlinear SBW system with time delay communications. The proposed adaptive active disturbance rejection control comprises the following two elements: (1) An adaptive extended state observer and (2) an adaptive state error feedback controller. The adaptive extended state observer with adaptive gains is employed for estimating the unmeasured velocity, acceleration, and compound disturbance which consists of system parameter uncertainties, nonlinear

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Peng, Chao-Chung, Nai-Jen Cheng, and Min-Che Tsai. "Application of an Output Filtering Method for an Unstable Wheel-Driven Pendulum System Parameter Identification." Electronics 12, no. 22 (2023): 4569. http://dx.doi.org/10.3390/electronics12224569.

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This research aims to apply an output filtering method to conduct the system parameter identification of an unstable wheel-driven pendulum system. First, the nonlinear dynamic model of the system is established by utilizing the Lagrangian dynamic theorem. Next, the Least-Square (LS) is introduced for system parameter identification formulation. Nevertheless, considering the real scenario, the wheel displacement is acquired from encoders subject to quantization errors. The pitch angle of the pendulum cart is also accompanied by Gaussian noise. Therefore, using numerical differentiation for angu

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Rodríguez-García, Marco A., Isaac Pérez Castillo, and P. Barberis-Blostein. "Efficient qubit phase estimation using adaptive measurements." Quantum 5 (June 4, 2021): 467. http://dx.doi.org/10.22331/q-2021-06-04-467.

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Estimating correctly the quantum phase of a physical system is a central problem in quantum parameter estimation theory due to its wide range of applications from quantum metrology to cryptography. Ideally, the optimal quantum estimator is given by the so-called quantum Cramér-Rao bound, so any measurement strategy aims to obtain estimations as close as possible to it. However, more often than not, the current state-of-the-art methods to estimate quantum phases fail to reach this bound as they rely on maximum likelihood estimators of non-identifiable likelihood functions. In this work we thoro

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Ruckstuhl, Y., and T. Janjić. "Combined State-Parameter Estimation with the LETKF for Convective-Scale Weather Forecasting." Monthly Weather Review 148, no. 4 (2020): 1607–28. http://dx.doi.org/10.1175/mwr-d-19-0233.1.

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Abstract We investigate the feasibility of addressing model error by perturbing and estimating uncertain static model parameters using the localized ensemble transform Kalman filter. In particular we use the augmented state approach, where parameters are updated by observations via their correlation with observed state variables. This online approach offers a flexible, yet consistent way to better fit model variables affected by the chosen parameters to observations, while ensuring feasible model states. We show in a nearly operational convection-permitting configuration that the prediction of

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