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Dissertations / Theses on the topic 'State and parameter estimations'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Kubitzki, Marcus. "State and Parameter Estimation in Quantum Theory." [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10806359.

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2

Evestedt, Magnus. "Parameter and State Estimation with Information-rich Signals." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8315.

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3

Wenzel, Thomas A. "State and parameter estimation for vehicle dynamic control." Thesis, Coventry University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422507.

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4

Carr, Matthew J. "State and parameter estimation techniques for stochastic systems." Thesis, University of Salford, 2006. http://usir.salford.ac.uk/26606/.

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This thesis documents research undertaken on state and parameter estimation techniques for stochastic systems in a maintenance context. Two individual problem scenarios are considered. For the first scenario, we are concerned with complex systems and the research involves an investigation into the ability to identify and quantify the occurrence of fault injection during routine preventive maintenance procedures. This is achieved using an appropriate delay time modelling specification and maximum-likelihood parameter estimation techniques. The delay time model of the failure process is paramete

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5

Hart, S. D. "Parameter optimisation and state estimation for machine control." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/843955/.

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This thesis is concerned with the modelling of electrical machines for use in variable-speed drives. Even when the structure of the model of the machine is known values have to be assigned to the parameters. In addition, it is usual for only some of the state variables to be measured, any others needed being estimated using the model. The present work is a study of methods of making on-line estimates of the model parameters, using a reduced number of measured states. To offer high level dynamic torque control the non-measured state variables must be indirectly estimated to a high degree of acc

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6

Creveling, Daniel R. "Parameter and state estimation in nonlinear dynamical systems." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3336421.

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Thesis (Ph. D.)--University of California, San Diego, 2008.<br>Title from first page of PDF file (viewed December 16, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 122-124).

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7

Schwarz, Lucia. "Error Models for Quantum State and Parameter Estimation." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18526.

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Within the field of Quantum Information Processing, we study two subjects: For quantum state tomography, one common assumption is that the experimentalist possesses a stationary source of identical states. We challenge this assumption and propose a method to detect and characterize the drift of nonstationary quantum sources. We distinguish diffusive and systematic drifts and examine how quickly one can determine that a source is drifting. Finally, we give an implementation of this proposed measurement for single photons. For quantum computing, fault-tolerant protocols assume that errors are

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8

Sawo, Felix. "Nonlinear state and parameter estimation of spatially distributed systems." Karlsruhe Univ.-Verl. Karlsruhe, 2009. http://d-nb.info/994500211/04.

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9

Evestedt, Magnus. "Parameter and state estimation using audio and video signals." Licentiate thesis, Uppsala : Univ. : Dept. of Information Technology, Univ, 2005. http://www.it.uu.se/research/publications/lic/2005-009/2005-009.pdf.

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10

Yu, Jia. "Distributed parameter and state estimation for wireless sensor networks." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28929.

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The research in distributed algorithms is linked with the developments of statistical inference in wireless sensor networks (WSNs) applications. Typically, distributed approaches process the collected signals from networked sensor nodes. That is to say, the sensors receive local observations and transmit information between each other. Each sensor is capable of combining the collected information with its own observations to improve performance. In this thesis, we propose novel distributed methods for the inference applications using wireless sensor networks. In particular, the efficient algor

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11

Stocks, Mikael. "Modeling, analysis, state and parameter estimation of faulty induction machines /." Luleå : Division of Systems and Interaction, Luleå University of Technology, 2006. http://epubl.ltu.se/1402-1544/2006/40/LTU-DT-0640-SE.pdf.

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12

Poyiadjis, George. "Particle methods for parameter estimation in general state space models." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614340.

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13

Sinha, Sumit. "Parameter estimation and auto-calibration of the STREAM-C model." Master's thesis, Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/content/templates/?a=72.

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14

Spataru, Mihai. "Battery aging diagnosis and prognosis for Hybrid Electrical Vehicles Applications." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366364019.

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15

Grip, Håvard Fjær. "Topics in State and Parameter Estimation for Nonlinear and Uncertain Systems." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-12081.

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This thesis is submitted in partial fulfillment of the requirements for the degree of Philosphiae Doctor (PhD) at the Norwegian University of Science and Technology (NTNU). The thesis is divided into three parts, each of which addresses a specific topic within state and parameter estimation for nonlinear and uncertain systems. Part I of the thesis is concerned with state and parameter estimation for automotive vehicles. The main focus is on estimation of the angle between the orientation of the vehicle and its direction of travel, known as the vehicle sideslip angle. The lateral dynamics of a

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16

Shahrrava, Behnam. "Indirect stochastic adaptive control using optimal joint parameter and state estimation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0006/NQ32855.pdf.

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17

Eigbe, Uwadiale. "State and parameter estimation in groundwater flow problems by Kalman filtering." Thesis, Imperial College London, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232864.

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18

Bizeray, Adrien. "State and parameter estimation of physics-based lithium-ion battery models." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:f326b332-b912-4bf6-a9b3-d5e61d3a9596.

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This thesis investigates novel algorithms for enabling the use of first-principle electrochemical models for battery monitoring and control in advanced battery management systems (BMSs). Specifically, the fast solution and state estimation of a high-fidelity spatially resolved thermal-electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional (P2D) model are investigated. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high

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19

Nemeth, Christopher. "Parameter estimation for state space models using sequential Monte Carlo algorithms." Thesis, Lancaster University, 2014. http://eprints.lancs.ac.uk/71713/.

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State space models represent a flexible class of Bayesian time series models which can be applied to model latent state stochastic processes. Sequential Monte Carlo (SMC) algorithms, also known as particle filters, are perhaps the most widely used methodology for inference in such models, particularly when the model is nonlinear and cannot be evaluated analytically. The SMC methodology allows for the sequential analysis of state space models in online settings for fast inference, but can also be applied to study online problems. This area of research has grown rapidly over the past 20 years an

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20

Jensen, John Petter. "Ensemble Kalman Filtering for State and Parameter Estimation on a Reservoir Model." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8742.

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<p>In reservoir management it is important with reservoir models that have good predictive abilities. Since the models initially are based on measurements with high uncertainties it is important to utilize new available data. Ensemble Kalman Filter (EnKF) is a new method for history matching that has received a lot of attention the last couple of years. This method is sequential and continuously update the reservoir model states (saturations, pressures etc.) and parameters (permeabilities, porosities etc) as data become available. The EnKF algorithm is derived and presented with a different n

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21

Weimer, Florian [Verfasser]. "Nonlinear State and Parameter Estimation for Small Unmanned Aircraft Systems / Florian Weimer." Aachen : Shaker, 2016. http://d-nb.info/1098038967/34.

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22

Oven, Sindre. "Leak Detection in Pipelines by the use of State and Parameter Estimation." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-24457.

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A model based estimation process is implemented in simulation of a transmissionline for the purpose of leak detection. The model has one inlet, one branch and twooutlets. The objective of this thesis is aimed at determining, through simulation results,the effectiveness of the Kalman Filter for leak detection in a pipeline network.Water distribution systems often contain a large amount of unknown losses. Themagnitude of the water loss range from 10 to over 50 percent of the total waterpumped. Water leaks and losses lead to an unnecessary increase in energy usedto pump water to users, which mean

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23

Lu, Zhen. "Application of Bayesian technology in state and parameter estimation for batch processes." Thesis, University of Newcastle Upon Tyne, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399112.

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24

Dube, Ntuthuko Marcus. "Development of methods for modelling, parameter and state estimation for nonlinear processes." Thesis, Cape Peninsula University of Technology, 2017. http://hdl.handle.net/20.500.11838/2619.

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Thesis (DTech (Electrical Engineering))--Cape Peninsula University of Technology, 2018.<br>Industrial processes tend to have very complex mathematical models that in most instances result in very model specific optimal estimation and designs of control strategies. Such models have many composition components, energy compartments and energy inventories that result in many process variables that are intertwined and too complex to separate from one another. Most of the derived mathematical process models, based on the application of first principles, are nonlinear and incorporate unknown pa

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25

Tate, Jennifer N. "Parameter estimation in tidally influenced numerical models determination of an appropriate objective function /." Master's thesis, Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-06252008-082239.

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26

Smith, Polly J. "Joint state and parameter estimation using data assimilation with application to morphodynamic modelling." Thesis, University of Reading, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533758.

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27

Cho, Kyong Rae. "Detection of broken rotor bars in induction motors using parameter and state estimation." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14343.

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28

Soane, Andrew. "Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29223.

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Particle filtering in stochastic volatility/jump models has gained significant attention in the last decade, with many distinguished researchers adding their contributions to this new field. Golightly (2009), Carvalho et al. (2010), Johannes et al. (2009) and Aihara et al. (2008) all attempt to extend the work of Pitt and Shephard (1999) and Liu and Chen (1998) to adapt particle filtering to latent state and parameter estimation in stochastic volatility/jump models. This dissertation will review their extensions and compare their accuracy at filtering the Bates stochastic volatility model. Add

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29

Wang, Peng. "Some improvements in state/parameter estimation using the cell-to-cell mapping technique /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486402544591959.

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30

Dutra, Dimas Abreu. "Maximum a posteriori joint state path and parameter estimation in stochastic differential equations." Universidade Federal de Minas Gerais, 2014. http://hdl.handle.net/1843/BUOS-9S3H9D.

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A wide variety of phenomena of engineering and scientific interest are of a continuous-time nature and can be modeled by stochastic differential equations (SDEs), which represent the evolution of the uncertainty in the states of a system. For systems of this class, some parameters of the SDE might be unknown and the measured data often includes noise, so state and parameter estimators are needed to perform inference and further analysis using the system state path. One such application is the flight testing of aircraft, in which flight path reconstruction or some other data smoothing technique

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31

Mosquera, Alonso Andrea. "State- and Parameter Estimation for Spacecraft with Flexible Appendages using Unscented Kalman Filters." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76320.

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The problem of system identification for dynamic effects on spacecraft has become increasingly relevant with the surge of agile spacecraft, which must perform large amplitude maneuvers at high rates. Precise knowledge of the state of the spacecraft, as well as of the parameters characterizing its motion, is vital for the design of control algorithms enabling stabilization and pointing accuracy. Traditional rigid body models and estimation methods are no longer sufficient to provide this knowledge. This thesis focuses on estimation of flexibility effects and spacecraft parameters through method

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32

Zhao, Junbo. "A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83423.

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The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These estimators should be robust to gross errors on the measurements and the model parameter values while providing good state estimates even in the presence of large dynamical system model uncertainties and non-Gaussian thick-tailed process and observation noises. It turns out that the current Kalman filter-based dynamic state estimators given in the literature suffer from several important shortcomings, precluding them fro

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33

Hodne, Kjetil. "Leak Detection in Two-Phase Oil and Gas Pipelines by Parameter- and State Estimation." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8862.

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<p>A two-fluid model is used to derive a set of boundary conditions. The conditions are produced numerically, and try to imitate the behavior of output injection by using a linearized version of the model. In order to ensure that the model is hyperbolic, virtual mass terms are included in the momentum equations. An observer is presented, using OLGA, a fluid simulator, as its model. The boundary conditions derived are employed in the observer, and its convergence properties are shown to improve. A set of adaption laws for estimating parameters in a two-phase leak model is derived. Estimation of

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34

Perez, Javier Rodriguez. "A hybrid strategy for improved state estimation in nonlinear lumped and distributed parameter models." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516501.

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35

Hopkins, Mark A. "Pseudo-linear identification: optimal joint parameter and state estimation of linear stochastic MIMO systems." Diss., Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/53941.

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This dissertation presents a new method of simultaneous parameter and state estimation for linear, stochastic, discrete—time, multiple-input, multiple-output (MIMO) (B systems. This new method is called pseudo·Iinear identification (PLID), and extends an earlier method to the more general case where system input and output measurements are corrupted by noise. PLID can be applied to completely observable, completely controllable systems with known structure (i.e., known observability indexes) and unknown parameters. No assumptions on pole and zero locations are required; and no assumptions on r

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36

Xu, Yijun. "Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/97876.

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It is a well-known fact that a power system contains many sources of uncertainties. These uncertainties coming from the loads, the renewables, the model and the measurement, etc, are influencing the steady state and dynamic response of the power system. Facing this problem, traditional methods, such as the Monte Carlo method and the Perturbation method, are either too time consuming or suffering from the strong nonlinearity in the system. To solve these, this Dissertation will mainly focus on developing the polynomial chaos based method to replace the traditional ones. Using it, the unce

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Al, zghool Raed Ahmad Hasan. "Estimation for state space models quasi-likelihood and asymptotic quasi-likelihood approaches /." Access electronically, 2008. http://ro.uow.edu.au/theses/91.

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38

Baby, Arun Paul. "Comparison of Modal Parameter Estimation using State Space Methods (N4SID) and the Unified Matrix Polynomial Approach." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613731964598438.

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39

Hu, Yiran. "Identification and State Estimation for Linear Parameter Varying Systems with Application to Battery Management System Design." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281368350.

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40

Schmitt, Andreas Joachim. "Power System Parameter Estimation for Enhanced Grid Stability Assessment in Systems with Renewable Energy Sources." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83459.

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The modern day power grid is a highly complex system; as such, maintaining stable operations of the grid relies on many factors. Additionally, the increased usage of renewable energy sources significantly complicates matters. Attempts to assess the current stability of the grid make use of several key parameters, however obtaining these parameters to make an assessment has its own challenges. Due to the limited number of measurements and the unavailability of information, it is often difficult to accurately know the current value of these parameters needed for stability assessment. This work a

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41

Senkel, Luise [Verfasser]. "Sliding Mode Techniques for Robust Control, State Estimation and Parameter Identification of Uncertain Dynamic Systems / Luise Senkel." Aachen : Shaker, 2018. http://d-nb.info/1159833230/34.

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42

Angwenyi, David [Verfasser], Sebastian [Akademischer Betreuer] Reich, and Claudia [Akademischer Betreuer] Stolle. "Time-continuous state and parameter estimation with application to hyperbolic SPDEs / David Angwenyi ; Sebastian Reich, Claudia Stolle." Potsdam : Universität Potsdam, 2019. http://d-nb.info/1219150053/34.

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43

Gorynin, Ivan. "Bayesian state estimation in partially observable Markov processes." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL009/document.

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Cette thèse porte sur l'estimation bayésienne d'état dans les séries temporelles modélisées à l'aide des variables latentes hybrides, c'est-à-dire dont la densité admet une composante discrète-finie et une composante continue. Des algorithmes généraux d'estimation des variables d'états dans les modèles de Markov partiellement observés à états hybrides sont proposés et comparés avec les méthodes de Monte-Carlo séquentielles sur un plan théorique et appliqué. Le résultat principal est que ces algorithmes permettent de réduire significativement le coût de calcul par rapport aux méthodes de Monte-

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44

Aktaran-Kalayci, Tuba. "Steady-State Analyses: Variance Estimation in Simulations and Dynamic Pricing in Service Systems." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/13993.

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In this dissertation, we consider analytic and numeric approaches to the solution of probabilistic steady-state problems with specific applications in simulation and queueing theory. Our first objective on steady-state simulations is to develop new estimators for the variance parameter of a selected output process that have better performance than certain existing variance estimators in the literature. To complete our analysis of these new variance estimators, called linear combinations of overlapping variance estimators, we do the following: establish theoretical asymptotic properties of th

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45

Jones, Caitlin Dawn. "Stellar variance for asteroseismic parameter estimation and inferences on the evolutionary state and binary population of red giant stars." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8444/.

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The latest generation of space missions have performed large scale observations of stars and this has been revolutionary in the field of asteroseismology. The ability to characterise thousands of stars has been instrumental in understanding the interiors of stars and the evolution of the Galaxy. This thesis focuses on studying red giant stars, both on an individual basis and as a population, using a robust asteroseismic metric we define based on the bandpass filtered estimate of the stellar variance. Here we present results of testing asteroseismic scaling relations, and the assumptions needed

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Singh, Kanwar Bharat. "Development of an Intelligent Tire Based Tire - Vehicle State Estimator for Application to Global Chassis Control." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76959.

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The contact between the tire and the road is the key enabler of vehicle acceleration, deceleration and steering. However, under the circumstances of sudden changes to the road conditions, the driver`s ability to maintain control of the vehicle maybe at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road contact parameters (ranging from kinematic conditions of the tire to its dynamic properties) are availab

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Gorynin, Ivan. "Bayesian state estimation in partially observable Markov processes." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL009.

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Cette thèse porte sur l'estimation bayésienne d'état dans les séries temporelles modélisées à l'aide des variables latentes hybrides, c'est-à-dire dont la densité admet une composante discrète-finie et une composante continue. Des algorithmes généraux d'estimation des variables d'états dans les modèles de Markov partiellement observés à états hybrides sont proposés et comparés avec les méthodes de Monte-Carlo séquentielles sur un plan théorique et appliqué. Le résultat principal est que ces algorithmes permettent de réduire significativement le coût de calcul par rapport aux méthodes de Monte-

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48

Rao, Sughosh J. "Development of a Hardware in the Loop Simulation System for Heavy Truck ESC Evaluation and Trailer Parameter and State Estimation." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374154113.

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Zhong, Shan. "Measurement calibration/tuning & topology processing in power system state estimation." Texas A&M University, 2003. http://hdl.handle.net/1969.1/1595.

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State estimation plays an important role in modern power systems. The errors in the telemetered measurements and the connectivity information of the network will greatly contaminate the estimated system state. This dissertation provides solutions to suppress the influences of these errors. A two-stage state estimation algorithm has been utilized in topology error identification in the past decade. Chapter II discusses the implementation of this algorithm. A concise substation model is defined for this purpose. A friendly user interface that incorporates the two-stage algorithm into the conve

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Acosta, Argueta Lesly María. "Particle filtering estimation for linear and nonlinear state-space models." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/134356.

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The sequential estimation of the states (filtering) and the corresponding simultaneous estimation of the states and fixed parameters of a dynamic state-space model, being linear or not, is an important probleminmany fields of research, such as in the area of finance. The main objective of this research is to estimate sequ entially and efficiently –from a Bayesian perspective via the particle filtering methodology– the states and/or the fixed parameters of a nonstandard dynamic state-spacemodel: one that is possibly nonlinear, non-stationary or non-Gaussian. The present thesis consists of sev

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