Dissertations / Theses on the topic 'Parameter identification'
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Manchu, Sreenivasarao. "Parameter Identification for Mechanical Joints." Thesis, Blekinge Tekniska Högskola, Avdelningen för maskinteknik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4309.
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All but the simplest physical systems contains mechanical joints. The behavior of these joints is sometimes the dominant factor in over all system behavior. The potential for occurence of microslip and macroslip normally makes the behavior of joints non-linear. Accurate modeling of joints requires a non-linear ramework. As clamping pressures are typically random ad variable, the behavior of the joints becomes random. Joint geometries are random along with other unknowns of the joints. Two different methods for measuring the energy dissipation are explained. In the experimental method, the energy dissipation of a non-linear joint is calculated from the slope of the envelope of the time response of acceleration. The simulation work is carried out by considering a smooth hysteresis model with the help of Matlab programming. Finally, the parameters are extracted for a specific non-linear system by comparing analytical and experimental results.0736988322
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Jais, Mathias. "Parameter identification for Maxwell's equations." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/54581/.
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In this work we present a variational algorithm to determine the parameters iir(x) and er(x) in the Maxwell system VxE + k xTH = 0, V x H - kerE = 0 in a body Q from boundary measurements of electromagnetic pairs (n x En dci,n x Hn dn), n= 1,2,…, where n is the outer unit normal. We show that this inverse problem can be solved by minimizing a positive functional C7(m,c) and using a conjugate gradient scheme. Apart from implementations with global boundary, we also consider the case of partial boundary, where we have only data available on a subset T C dQ. Further do we develop uniqueness results, to show that the given data (n x En dn, n x Hn dn), n = 1,2,…, is a sufficient basis to solve the inverse problem. We investigate the uniqueness properties of the inverse problem in the case of global boundary data as well as in the case of partial boundary data. To show the effectivness and the stability of our approach we present various numerical results with noisy data. Finally we outline an alternative method, where one is only interested in recovering the support of the functions fi l 1 and er 1.
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Norris, Mark A. "Parameter identification in distributed structures." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/71164.
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This dissertation develops two new techniques for the identification of parameters in distributed-parameter systems. The first technique identifies the physical parameter distributions such as mass, damping and stiffness. The second technique identifies the modal quantities of self-adjoint distributed-parameter systems.
Distributed structures are distributed-parameter systems characterized by mass, damping and stiffness distributions. To identify the distributions, a new identification technique is introduced based on the finite element method. With this approach, the object is to identify "average" values of mass, damping and stiffness distributions over each finite element. This implies that the distributed parameters are identified only approximately, in the same way in which the finite element method approximates the behavior of a structure.
It is common practice to represent the motion of a distributed parameter system by a linear combination of the associated modes of vibration. In theory, we have an infinite set of modes although, in practice we are concerned with only a finite linear combination of the modes. The modes of vibration possess certain properties which distinguish them from one another. Indeed, the modes of vibration are uncorrelated in time and orthogonal in space. The modal identification technique introduced in this dissertation uses path these spatial properties. Because both the temporal and spatial properties are used, the method does not encounter problems when the natural frequencies are closely-spaced or repeated.Ph. D.
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Sui, Liqi. "Uncertainty management in parameter identification." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2330/document.
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Afin d'obtenir des simulations plus prédictives et plus précises du comportement mécanique des structures, des modèles matériau de plus en plus complexes ont été développés. Aujourd'hui, la caractérisation des propriétés des matériaux est donc un objectif prioritaire. Elle exige des méthodes et des tests d'identification dédiés dans des conditions les plus proches possible des cas de service. Cette thèse vise à développer une méthodologie d'identification efficace pour trouver les paramètres des propriétés matériau, en tenant compte de toutes les informations disponibles. L'information utilisée pour l'identification est à la fois théorique, expérimentale et empirique : l'information théorique est liée aux modèles mécaniques dont l'incertitude est épistémique; l'information expérimentale provient ici de la mesure de champs cinématiques obtenues pendant l'essai ct dont l'incertitude est aléatoire; l'information empirique est liée à l'information à priori associée à une incertitude épistémique ainsi. La difficulté principale est que l'information disponible n'est pas toujours fiable et que les incertitudes correspondantes sont hétérogènes. Cette difficulté est surmontée par l'utilisation de la théorie des fonctions de croyance. En offrant un cadre général pour représenter et quantifier les incertitudes hétérogènes, la performance de l'identification est améliorée. Une stratégie basée sur la théorie des fonctions de croyance est proposée pour identifier les propriétés élastiques macro et micro des matériaux multi-structures. Dans cette stratégie, les incertitudes liées aux modèles et aux mesures sont analysées et quantifiées. Cette stratégie est ensuite étendue pour prendre en compte l'information à priori et quantifier l'incertitude associéeIn order to obtain more predictive and accurate simulations of mechanical behaviour in the practical environment, more and more complex material models have been developed. Nowadays, the characterization of material properties remains a top-priority objective. It requires dedicated identification methods and tests in conditions as close as possible to the real ones. This thesis aims at developing an effective identification methodology to find the material property parameters, taking advantages of all available information. The information used for the identification is theoretical, experimental, and empirical: the theoretical information is linked to the mechanical models whose uncertainty is epistemic; the experimental information consists in the full-field measurement whose uncertainty is aleatory; the empirical information is related to the prior information with epistemic uncertainty as well. The main difficulty is that the available information is not always reliable and its corresponding uncertainty is heterogeneous. This difficulty is overcome by the introduction of the theory of belief functions. By offering a general framework to represent and quantify the heterogeneous uncertainties, the performance of the identification is improved. The strategy based on the belief function is proposed to identify macro and micro elastic properties of multi-structure materials. In this strategy, model and measurement uncertainties arc analysed and quantified. This strategy is subsequently developed to take prior information into consideration and quantify its corresponding uncertainty
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Kraft, Sönke. "Parameter identification for a TGV model." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00731143.
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This work investigates the applicability of identification methods to the suspension parameters of a TGV multi-body model. The aim is to adjust the model to the real system by estimating the suspension parameters from measured vehicle response data. Due to the nonlinear behavior of the system the time-domain based model updating has been chosen. It requires the definition and minimization of a misfit function in the time domain describing the distance between model and measurement. The fastest convergence is obtained by the use of gradient methods requiring the calculation of the derivatives of the misfit function relative to every parameter. Since the calculation from finite differences is time consuming and less accurate the gradients are calculated from the adjoint method. The application to a simplified bogie model with known mathematical description allows the identification of its suspension parameters. The presence of local minima in the misfit function of the TGV model requires the use of global optimization methods. The simulated annealing and the genetic algorithm method give important reductions of the misfit function and improved parameter estimations. In following work this information could be used for further applications like the condition monitoring.
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Drexel, Michael V. "Modal parameter identification using mode isolation." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/17239.
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Rückert, Nadja, Robert S. Anderssen, and Bernd Hofmann. "Stable Parameter Identification Evaluation of Volatility." Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-85402.
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Using the dual Black-Scholes partial differential equation, Dupire derived an explicit formula, involving the ratio of partial derivatives of the evolving fair value of a European call option (ECO), for recovering information about its variable volatility. Because the prices, as a function of maturity and strike, are only available as discrete noisy observations, the evaluation of Dupire’s formula reduces to being an ill-posed numerical differentiation problem, complicated by the need to take the ratio of derivatives. In order to illustrate the nature of ill-posedness, a simple finite difference scheme is first used to approximate the partial derivatives.
A new method is then proposed which reformulates the determination of the volatility, from the partial differential equation defining the fair value of the ECO, as a parameter identification activity. By using the weak formulation of this equation, the problem is localized to a subregion on which the volatility surface can be approximated by a constant or a constant multiplied by some known shape function which models the local shape of the volatility function. The essential regularization is achieved through the localization, the choice of the analytic weight function, and the application of integration-by-parts to the weak formulation to transfer the differentiation of the discrete data to the differentiation of the analytic weight function.
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Steele, Andrew D. "Time constrained qualitative model-based parameter identification." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/735.
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Iacobucci, Marco. "Dynamic parameter identification of a collaborative robot." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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I robot collaborativi stanno guadagnando un interesse crescente nel campo della robotica. Dal momento che l'industria 4.0 richiede nuovi livelli di flessibilità e soluzioni innovative di prodotto, robot e umani recentemente hanno iniziato ad interagire e lavorare in un 'ambiente comune senza protezioni perimetrali. Questa tecnologia emergente può provvedere all'operatore supporto fisico o assistenza nello svolgere compiti pericolosi o faticosi. In questo scenario, un controllo in tempo reale dovrebbe essere il più affidabile possibile e minimizzare ogni rischio legato alla collaborazione tra uomo e robot.
L'obiettivo di questa tesi è l'identificazione dei coefficienti dinamici che linearizzano il modello del robot e dei parametri dinamici (massa, posizione del centro di massa ed elementi dei tensori d'inerzia di ciascun membro), utili per simulare il comportamento del robot in ambiente CAD, per ottenere simulazioni dinamiche più realistiche e algoritmi di controllo in tempo reale più affidabili.
Un approccio di identificazione dinamica è presentato per il Franka Emika Panda, un robot collaborativo a 7 gradi di libertà. Questo consiste nel suddividere l'identificazione in due fasi: una prima fase in cui si analizzano le sole configurazioni statiche del robot per ottenere un set di possibili masse e centri di massa, ed una seconda fase in cui si considera il robot in movimento ed è possibile ottenere alcuni valori degli elementi dei tensori d'inerzia. Seguendo questo approccio, è possibile ottenere una stima più precisa dei parametri di massa e di posizione dei centri di massa rispetto ad un approccio in cui l'identificazione viene compiuta in una singola fase, cosa che è stata successivamente dimostrata da alcuni test eseguiti sul robot stesso, i cui risultati sono stati confrontati con quelli ottenuti seguendo un altro approccio e quelli restituiti direttamente dalla libreria del robot.
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Alami, Mohsen. "Interval Based Parameter Identification for System Biology." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-75161.
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This master thesis studies the problem of parameter identification for system biology. Two methods have been studied. The method of interval analysis uses subpaving as a class of objects to manipulate and store inner and outer approximations of compact sets. This method works well with the model given as a system of differential equations, but has its limitations, since the analytical expression for the solution to the ODE is not always obtainable, which is needed for constructing the inclusion function. The other method, studied, is SDP-relaxation of a nonlinear and non-convex feasibility problem. This method, implemented in the toolbox bio.SDP, works with system of difference equations, obtained using the Euler discretization method. The discretization method is not exact, raising the need of bounding this discretization error. Several methods for bounding this error has been studied. The method of ∞-norm optimization, also called worst-case-∞-norm is applied on the one-step error estimation method. The methods have been illustrated solving two system biological problems and the resulting SCP have been compared.Det här examensarbetet studerar problemet med parameteridentifiering för systembiologi. Två metoder har studerats. Metoden med intervallanalys använder union av intervallvektorer som klass av objekt för att manipulera och bilda inre och yttre approximationer av kompakta mängder. Denna metod fungerar väl för modeller givna som ett system av differentialekvationer, men har sina begränsningar, eftersom det analytiska uttrycket för lösningen till differentialekvationen som är nödvändigt att känna till för att kunna formulera inkluderande funktioner, inte alltid är tillgängliga. Den andra studerade metoden, använder SDP-relaxering, som ett sätt att komma runt problemet med olinjäritet och icke-konvexitet i systemet. Denna metod, implementerad i toolboxen bio.SDP, utgår från system av differensekvationer, framtagna via Eulers diskretiserings metod. Diskretiseringsmetoden innehåller fel och osäkerhet, vilket gör det nödvändigt att estimera en gräns för felets storlek. Några felestimeringsmetoder har studerats. Metoden med ∞-norm optimering, också kallat worst-case-∞-norm är tillämpat på ett-stegs felestimerings metoder. Metoderna har illustrerats genom att lösa två system biologiska problem och de accepterade parametermängderna, benämnt SCP, har jämförts och diskuterats.
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Zhou, Wenliang. "Multivariate analysis in vibration modal parameter identification /." View online ; access limited to URI, 2006. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3248248.
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Liu, Yi. "Grey-box Identification of Distributed Parameter Systems." Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220.
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McGrail, Amanda K. "OnBoard Parameter Identification for a Small UAV." Thesis, West Virginia University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1522521.
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One of the main research focus areas of the WVU Flight Control Systems Laboratory (FCSL) is the increase of flight safety through the implementation of fault tolerant control laws. For some fault tolerant flight control approaches with adaptive control laws, the availability of accurate post failure aircraft models improves performance. While look-up tables of aircraft models can be created for failure conditions, they may fail to account for all possible failure scenarios. Thus, a real-time parameter identification program eliminates the need to have predefined models for all potential failure scenarios. The goal of this research was to identify the dimensional stability and control derivatives of the WVU Phastball UAV in flight using a frequency domain based real-time parameter identification (PID) approach.
The data necessary for this project was gathered using the WVU Phastball UAV, a radio-controlled aircraft designed and built by the FCSL for fault tolerant control research. Maneuvers designed to excite the natural dynamics of the aircraft were implemented by the pilot or onboard computer during the steady state portions of flights. The data from these maneuvers was used for this project.
The project was divided into three main parts: 1) off-line time domain PID, 2) off-line frequency domain PID, and 3) an onboard frequency domain PID. The off-line parameter estimation programs, in both frequency domain and time domain, utilized the well known Maximum Likelihood Estimator with Newton-Raphson minimization with starting values estimated from a Least-Squares Estimate of the non-dimensional stability and control derivatives. For the frequency domain approach, both the states and inputs were first converted to the frequency domain using a Fourier integral over the frequency range in which the rigid body aircraft dynamics are found. The final phase of the project was a real-time parameter estimation program to estimate the dimensional stability and control derivatives onboard the Phastball aircraft. A frequency domain formulation of the least-squares estimation process was used because of its low computational and memory requirements and robustness to measurement noise and sensor information dropouts. Most of the onboard parameter estimates obtained converge to the values determined using the off-line parameter estimation programs (though a few typically show a bias) within four to six seconds for longitudinal estimates and four to eight seconds for the later estimates. For the experiments conducted, the real-time parameter estimates did not diverge after the conclusion of the maneuver.
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Chander, R. "Identification of distributed parameter systems with damping." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/13386.
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Kampisios, Konstantinos T. "Electrical machines parameter identification using genetic algorithms." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/14005/.
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In Indirect Field Orientation (IFO) of induction motors, the interest for parameters identification has increased rapidly due to the great demand for high performance drives and more sophisticated control systems that have been made possible by the development of very powerful processors, such as floating point DSPs. Accurate knowledge of the machine electrical parameters is also required to ensure correct alignment of the stator current vector relative to the rotor flux vector, to decouple the flux - and torque - producing currents and to tune the current control loops. The accuracy and general robustness of the machine is dependant on this model. Artificial intelligent technologies have been tested in the field of electro mechanics like neural networks, fuzzy logic, simulated annealing and genetic algorithms. These methods are increasingly being utilised in solving electric machine problems. This thesis addresses a novel non - intrusive approach for identifying induction motor equivalent circuit parameters based on experimental transient measurements from a vector controlled Induction Motor (I.M.) drive and using an off line Genetic Algorithm (GA) routine with a linear machine model. The evaluation of the electrical motor parameters at rated flux operation is achieved by minimising the error between experimental responses (speed or current) measured on a motor drive and the respective ones obtained by a simulation model based on the same control structure as the experimental rig. An accurate and fast estimation of the electrical motor parameters is so achieved. Results are verified through a comparison of speed, torque and line current responses between the experimental IM drive and a Matlab - Simulink model. The second part of the research work introduces a new approach based on heuristic optimisation for identifying induction motor electrical parameters under different operating conditions such as different load and flux levels. Results show via interpolation test the effect of the most important electrical parameters, the magnetising inductance Lm and rotor resistance Rr, at each different operating condition.
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Wade, Scott. "Parameter identification for vector controlled induction machines." Thesis, Heriot-Watt University, 1995. http://hdl.handle.net/10399/1311.
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Pokhrel, Prafulla. "TOWARDS IMPROVED IDENTIFICATION OF SPATIALLY-DISTRIBUTED RAINFALL RUNOFF MODELS." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/194356.
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Distributed rainfall runoff hydrologic models can be highly effective in improving flood forecasting capabilities at ungauged, interior locations of the watershed. However, their implementation in operational decision-making is hindered by the high dimensionality of the state-parameter space and by lack of methods/understanding on how to properly exploit and incorporate available spatio-temporal information about the system. This dissertation is composed of a sequence of five studies, whose overall goal is to improve understanding on problems relating to parameter identifiability in distributed models and to develop methodologies for their calibration.The first study proposes and investigates an approach for calibrating catchment scale distributed rainfall-runoff models using conventionally available data. The process, called regularization, uses spatial information about soils and land-use that is embedded in prior parameter estimates (Koren et al. 2000) and knowledge of watershed characteristics, to constrain and reduce the dimensionality of the feasible parameter space.The methodology is further extended in the second and third studies to improve extraction of `hydrologically relevant' information from the observed streamflow hydrograph. Hydrological relevance is provided by using signature measures (Yilmaz et al 2008) that correspond to major watershed functions. While the second study applies a manual selection procedure to constrain parameter sets from the subset of post calibrated solutions, the third develops an automatic procedure based on a penalty function optimization approach.The fourth paper investigates the relative impact of using the commonly used multiplier approach to distributed model calibration, in comparison with other spatial regularization strategies and also includes investigations on whether calibration to data at the catchment outlet can provide improved performance at interior locations. The model calibration study conducted for three mid sized catchments in the US led to the important finding that basin outlet hydrographs might not generally contain information regarding spatial variability of the parameters, and that calibration of the overall mean of the spatially distributed parameter fields may be sufficient for flow forecasting at the outlet. This then was the motivation for the fifth paper which investigates to what degree the spatial characteristics of parameter and rainfall fields can be observable in catchment outlet hydrographs.
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Bennia, Abdelhak. "Mimo systems parameters identification." Thesis, Virginia Tech, 1986. http://hdl.handle.net/10919/41579.
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In this thesis, a presentation of a new canonical representation of multi-input multioutput systems is given. The new characterization covers the full range of practical situations in linear systems according to the structural properties and model of the perturbations which are known. Its direct link to ARMA processes as well as to classical state space representation ls also given.
The importance of the new representation lies in the fact that all unknown parameters and state variables appear linearly multlplied by either external variables (inputs and outputs) that appear in the data record, or by matrices that are only composed of ieroes and ones. This property enables us to perform a joint state and parameters estimation. Moreover, if the noises are gaussian and their statistics are known, an on-line algorithm that involves a standard dlscrete-time time-varying Kalman filter is proposed and used successfully in the estimation of unknown parameters for simulated examples.
Master of Science
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Johnson, Jay H. "AUV steering parameter identification for improved control design." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA397498.
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Thesis (M.S. in Mechanical Engineering) Naval Postgraduate School, June 2001.Thesis advisor(s): Healey, Anthony J. "June 2001." Includes bibliographical references (p. 55). Also Available in print.
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Vexler, Boris. "Adaptive finite element methods for parameter identification problems." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=971435170.
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Sabade, Sagar Suresh. "Integrated circuit outlier identification by multiple parameter correlation." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/267.
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Semiconductor manufacturers must ensure that chips conform to their specifications before they are shipped to customers. This is achieved by testing various parameters of a chip to determine whether it is defective or not. Separating defective chips from fault-free ones is relatively straightforward for functional or other Boolean tests that produce a go/no-go type of result. However, making this distinction is extremely challenging for parametric tests. Owing to continuous distributions of parameters, any pass/fail threshold results in yield loss and/or test escapes. The continuous advances in process technology, increased process variations and inaccurate fault models all make this even worse. The pass/fail thresholds for such tests are usually set using prior experience or by a combination of visual inspection and engineering judgment. Many chips have parameters that exceed certain thresholds but pass Boolean tests. Owing to the imperfect nature of tests, to determine whether these chips (called "outliers") are indeed defective is nontrivial. To avoid wasted investment in packaging or further testing it is important to screen defective chips early in a test flow. Moreover, if seemingly strange behavior of outlier chips can be explained with the help of certain process parameters or by correlating additional test data, such chips can be retained in the test flow before they are proved to be fatally flawed. In this research, we investigate several methods to identify true outliers (defective chips, or chips that lead to functional failure) from apparent outliers (seemingly defective, but fault-free chips). The outlier identification methods in this research primarily rely on wafer-level spatial correlation, but also use additional test parameters. These methods are evaluated and validated using industrial test data. The potential of these methods to reduce burn-in is discussed.
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El-Gamal, Mohamed A. "Fault location and parameter identification in analog circuits." Ohio : Ohio University, 1990. http://www.ohiolink.edu/etd/view.cgi?ohiou1172776742.
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Song, Xiaohui 1974. "The parameter identification of a novel speed reducer /." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33994.
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Many a mechanical application involves power transmission from a high-speed motor to a low-speed load. However, existing speed-reduction mechanisms are usually a major sink of energy and information in mechanical transmissions. Energy and positioning information are lost through: (a) friction between sliding components; (b) compliance; and (c) backlash. A novel transmission for speed reduction, Speed-o-Cam, is currently under research at McGill University's Centre for Intelligent Machines (CIM). The transmission is based on the layout of pure-rolling indexing cam mechanisms, and hence, eliminates backlash and friction. Besides zero backlash and low friction losses, Speed-o-Cam also offers the possibility of high stiffness, another essential attribute for high-accuracy applications.This thesis focuses on the aspects of both model development and mechanical-parameter identification of a spherical prototype of Speed-o-Cam. Our main interest lies in identifying the mechanism stiffness. In order to conduct experiments on the prototype, a testbed was designed and fabricated. A mathematical model of the testbed is first formulated. Based on this model and the results of experiments, the parameters of the Speed-o-Cam prototype are identified. In the process, the stiffness and damping parameters of the couplings of the testbed are also identified.
Power efficiency is an important indicator of speed reducing mechanisms. For the Speed-o-Cam prototype, this indicator is also estimated experimentally.
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Joseph, Daniel Scott. "Parameter Identification for the Preisach Model of Hysteresis." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/27295.
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Hysteresis, defined as a rate independent memory
effect, is a phenomenon that occurs in many
physical systems. The effect is sometimes
desired, sometimes a nuisance, sometimes cata-
strophic, but in every case we must understand
hysteresis if we are to better understand the
system itself.
This work introduces a method of parameter
identification for the Preisach model of hyster-
esis. This identification method is explored
in both the setting of non-singular measures
and the setting of singular measures.Ph. D.
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Ozarkar, Malhar. "Design Parameter Identification and Verification for Thermoplastic Inserts." Thesis, Linköpings universitet, Mekanik och hållfasthetslära, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170132.
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Inserts are a crucial part of household and industrial furniture. These small plastic parts which often go unnoticed to the naked eye perform crucial functions like providing a base for the furniture, leveling the furniture, safeguarding the user from edges of the tubes used and providing an aesthetic finish. The inserts have a wing like structure on the exterior which enables them to be inserted and securely held in the tubes. The inserts are assembled into the pipes manually or through machines. The force required to install these inserts in the tube is called a push-in force whereas a pull-out force is the force required for removal of the is called a pull-out force. These forces are experienced by someone who assembles the furniture together. Thus, these forces directly define the ease with which the furniture can be assembled. In the first part of the present thesis, these push-in and pull-out forces are predicted using finite element simulations. These finite element simulations were validated by performing physical assembly and disassembly experiments on these inserts. It was found that the finite element simulations of the insert are useful tool in predicting the push-in forces with a high accuracy. These push-in and pull-out forces for a single insert vary by 2-5 times when the dimensional variations in the tube are considered. The dimensional variations can be a result of the manufacturing processes from which these tubes are produced. The maximum and minimum dimensions that the tube can have are defined by the maximum material condition (MMC) and the least material condition (LMC). To reduce the variation in push-in and pull out forces, a stricter tolerance control can be applied to the manufacturing process. To avoid this cost while having a lower variation in the push-in and pull out forces, the design of the insert was modified. To achieve this enhanced design of the insert, a metamodel based optimization technique was used in the second part of the thesis. For this optimization, the geometrical parameters - wing height, wing diameter and stem thickness the of the insert were identified as the crucial factors which govern the assembly/disassembly forces. The identification of these parameters was done through a design of experiments. These parameters were then varied simultaneously in a metamodel based optimization which had an objective to minimize the variation in forces observed for an insert when the maximum material condition and the least material conditions are considered. The result for the enhanced design of the insert was then stated in terms of the ratio of these identified parameters. The modified design of the insert not only enables the manufacturer to have better performance, but also reduces the amount of plastic material required for manufacturing of the insert.
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Martinsson, Jesper. "Ultrasonic measurement principles : modeling, identification, and parameter estimation /." Luleå : Luleå University of Technology, 2008. http://epubl.luth.se/1402-1544/2008/37.
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Vazirinejad, Shamsedin. "Model identification and parameter estimation of stochastic linear models." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185037.
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It is well known that when the input variables of the linear regression model are subject to noise contamination, the model parameters can not be estimated uniquely. This, in the statistical literature, is referred to as the identifiability problem of the errors-in-variables models. Further, in linear regression there is an explicit assumption of the existence of a single linear relationship. The statistical properties of the errors-in-variables models under the assumption that the noise variances are either known or that they can be estimated are well documented. In many situations, however, such information is neither available nor obtainable. Although under such circumstances one can not obtain a unique vector of parameters, the space, Ω, of the feasible solutions can be computed. Additionally, assumption of existence of a single linear relationship may be presumptuous as well. A multi-equation model similar to the simultaneous-equations models of econometrics may be more appropriate. The goals of this dissertation are the following: (1) To present analytical techniques or algorithms to reduce the solution space, Ω, when any type of prior information, exact or relative, is available; (2) The data covariance matrix, Σ, can be examined to determine whether or not Ω is bounded. If Ω is not bounded a multi-equation model is more appropriate. The methodology for identifying the subsets of variables within which linear relations can feasibly exist is presented; (3) Ridge regression technique is commonly employed in order to reduce the ills caused by collinearity. This is achieved by perturbing the diagonal elements of Σ. In certain situations, applying ridge regression causes some of the coefficients to change signs. An analytical technique is presented to measure the amount of perturbation required to render such variables ineffective. This information can assist the analyst in variable selection as well as deciding on the appropriate model; (4) For the situations when Ω is bounded, a new weighted regression technique based on the computed upper bounds on the noise variances is presented. This technique will result in identification of a unique estimate of the model parameters.
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Tang, Yun-chung. "Motor simulation and parameter identification in a reciprocating mechanism." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-10312009-020104/.
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Kent, W. F. "Machine learning for parameter identification of electric induction machines." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399178.
Full textAbstract:
This thesis is concerned with the application of simulated evolution (SE) to the steady-state parameter identification problem of a simulated and real 3-phase induction machine, over the no-load direct-on-line start period. In the case of the simulated 3-phase induction machine, the Kron's two-axis dynamic mathematical model was used to generate the real and simulated system responses where the induction machine parameters remain constant over the entire range of slip. The model was used in the actual value as well as the per-unit system, and the parameters were estimated using both the genetic algorithm (GA) and the evolutionary programming (EP) from the machine's dynamic response to a direct-on-line start. Two measurement vectors represented the dynamic responses and all the parameter identification processes were subject to five different levels of measurement noise. For the case of the real 3-phase induction machine, the real system responses were generated by the real 3-phase induction machine whilst the simulated system responses were generated by the Kron's model. However, the real induction machine's parameters are not constant over the range of slip, because of the nonlinearities caused by the skin effect and saturation. Therefore, the parameter identification of a real3-phase induction machine, using EP from the machine's dynamic response to a direct-on-line start, was not possible by applying the same methodology used for estimating the parameters of the simulated, constant parameters, 3-phase induction machine.
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Mustata, Radu. "Parameter identification within a porous medium using genetic algorithms." Thesis, University of Leeds, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400722.
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Baek, Youn Hyeong. "An experimental review of some aircraft parameter identification techniques." Thesis, Cranfield University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285023.
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Abeliuk, R. "Parameter identification in unsaturated flow and solute transport models." Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/38208.
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KojiÄ, Aleksandar M. 1974. "Global parameter identification and control of nonlinearly parameterized systems." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8330.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2002.Includes bibliographical references (leaves 109-114).
Nonlinearly parameterized (NLP) systems are ubiquitous in nature and many fields of science and engineering. Despite the wide and diverse range of applications, there exist relatively few results in control systems literature which exploit the structure of the nonlinear parameterization. A vast majority of presently applicable global control design approaches to systems with NLP, make use of either feedback-linearization, or assume linear parameterization, and ignore the specific structure of the nonlinear parameterization. While this type of approach may guarantee stability, it introduced three major drawbacks. First, they produce no additional information about the nonlinear parameters. Second, they may require large control authority and actuator bandwidth, which makes them unsuitable for some applications. Third, they may simply result in unacceptably poor performance. All of these inadequacies are amplified further when parametric uncertainties are present. What is necessary is a systematic adaptive approach to identification and control of such systems that explicitly accommodates the presence of nonlinear parameters that may not be known precisely. This thesis presents results in both adaptive identification and control of NLP systems. An adaptive controller is presented for NLP systems with a triangular structure. The presence of the triangular structure together with nonlinear parameterization makes standard methods such as back-stepping, and variable structure control inapplicable. A concept of bounding functions is combined with min-max adaptation strategies and recursive error formulation to result in a globally stabilizing controller.
(cont.) A large class of nonlinear systems including cascaded LNL (linear-nonlinear-linear) systems are shown to be controllable using this approach. In the context of parameter identification, results are derived for two classes of NLP systems. The first concerns systems with convex/concave parameterization, where min-max algorithms are essential for global stability. Stronger conditions of persistent excitation are shown to be necessary to overcome the presence of multiple equilibrium points which are introduced due to the stabilization aspects of the min-max algorithms. These conditions imply that the min-max estimator must periodically employ the local gradient information in order to guarantee parameter convergence. The second class of NLP systems considered in this concerns monotonically parameterized systems, of which neural networks are a specific example. It is shown that a simple algorithm based on local gradient information suffices for parameter identification. Conditions on the external input under which the parameter estimates converge to the desired set starting from arbitrary values are derived. The proof makes direct use of the monotonicity in the parameters, which in turn allows local gradients to be self-similar and therefore introduces a desirable invariance property. By suitably exploiting this invariance property and defining a sequence of distance metrics, global convergence is proved. Such a proof of global convergence is in contrast to most other existing results in the area of nonlinear parameterization, in general, and neural networks in particular.
by Aleksandar M. KojiÄ.
Ph.D.
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Toromanovic, Jasmina. "On Parameter Identification for Better Predictions of Dam Behaviour." Licentiate thesis, Luleå tekniska universitet, Geoteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-68474.
Full textAbstract:
Numerical modelling is often needed as a tool to predict the behaviour and assess the safety of dam structures. Embankment dam structures analyses are quite complex and potential failures are hazardous. Predictions of dam behaviour by numerical modelling rely on knowledge about the mechanical properties of the materials the dam is constructed with. The materials included in a dam vary significantly because zones in the dam have different functions. In order to conduct reliable modelling, parameter values defining the stress-strain relationship of the materials are needed to be assigned. Obtaining information about the mechanical behaviour in already existing embankment dams is usually challenging. As many dams are old, there might be a limited amount of information available of the materials used, construction methods and mostly about the stress-strain relationship of the soil. Traditionally, field sampling is performed in order to obtain such information. However, conventional field sampling might negatively affect the dam body and thereby the performance as well as the safety of the dam. This is of special importance if sampling is performed in the impervious (core) part. Since traditional sampling might harm the dam body, use of non-destructive methods would be advantageous to utilise for obtaining information about the stress-strain relationship and the strength in a dam structure. An option for a non-destructive method is parameter identification by inverse analysis. The idea of inverse analysis is to calibrate finite element models towards field measurements. In the calibration process, the input for a stress-strain relationship (constitutive model) is modified until the discrepancy between the output of the numerical model and the associated chosen field measurement is minimised. The agreement between output from the numerical model and reality is measured by an objective function that will calculate the error. In order to automatically search for the minimum a search algorithm is utilised in the optimisation process. When the objective function is minimised, the calibration of the material parameters is done. In previous research at Luleå University of Technology, the method of inverse analysis was applied to an embankment dam. The finite element program PLAXIS was used in combination with an optimisation code. The optimisation code includes an objective function (for error evaluation) and a search algorithm. The genetic algorithm was employed as search algorithm, since it is known for its robustness and efficiency as well as the fact that it provides a set of solutions instead of one unique answer. This is beneficial from a geotechnical point of view, since engineering judgement can be included in the final choice of solution. The first study in the present thesis deals with a case study of an embankment dam, where a simple model calibration was performed. This was a part of a larger study, at the ICOLD Benchmark Workshop in 2017, where the work presented here was forming one of the contributions. In order to have a model response similar to reality, the contributors were asked to choose constitutive models and calibrate them. The calibration was done by manually changing the input for the constitutive model chosen. While the response of the numerical finite element model was capturing the trends of measured total stresses and pore pressure in the dam quite well, there were difficulties in capturing the long term deformations of the dam. This was a challenge for all contributors. An idea for improving the model response, is to run a more advanced calibration by inverse analysis. In the second study in the thesis, predictions are presented for the embankment dam that inverse analysis was previously conducted for at LTU. Strengthening actions in form of a new berm were performed at the dam. With identified material parameter values from the inverse analysis, predictions were conducted both before and after the strengthening measures. The predicted deformations were compared to deformation data from inclinometer measurements. A reasonably well agreement was obtained with the real deformations. The trend of the deformations was replicated and the magnitudes of the deformations were in the right order. The study is indicating that predicting future dam behaviour based on results from inverse analysis can be done reasonably well. In the third and final study in the thesis, effects of random measurement error on the performance of the genetic algorithm for soil parameter identification are assessed. Also here, with the application to the embankment dam used in previous research at LTU. Optimisations were performed against inclinometer measurements. To be sure that the constitutive model can find the correct solution, synthetic (i.e. numerically generated) inclinometer data was utilised. Perturbations were randomly generated within chosen intervals of error and added to the numerically generated deformations. The genetic algorithm showed its robustness, by continuing to search for solutions without breaking down even if the field data was substantially perturbed. Considering usual errors for inclinometer measurements, the genetic algorithm can deliver good solutions. The inclinometer errors used were taken from literature, and thereafter related to the perturbations of the numerically generated data. Dealing with errors that are becoming gradually larger than what can be considered as usual, problems are faced by the genetic algorithm. In this cases it is difficult to find a solution, and if solutions are found they might significantly deviate from the unperturbed optimum solution. The three studies handled in this thesis are treating aspects of back analysis of embankment dams; from a simple calibration, to predictions based on material parameters from advanced inverse analysis and finally effects of errors on the genetic algorithm. It been shown that using inverse analysis for already existing embankment dams is very beneficial for the material characterisation and is forming a step towards better predictions of future dam behaviour.
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Hammer, Patricia W. "Parameter identification in parabolic partial differential equations using quasilinearization." Diss., Virginia Tech, 1990. http://hdl.handle.net/10919/37226.
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We develop a technique for identifying unknown coefficients in parabolic partial differential equations. The identification scheme is based on quasilinearization and is applied to both linear and nonlinear equations where the unknown coefficients may be spatially varying.
Our investigation includes derivation, convergence, and numerical testing of the quasilinearization based identification schemePh. D.
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Chou, I.-Chun. "Parameter estimation and network identification in metabolic pathway systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26513.
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Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009.Committee Chair: Voit, Eberhard O.; Committee Member: Borodovsky, Mark; Committee Member: Butera, Robert; Committee Member: Kemp, Melissa; Committee Member: Park, Haesun. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Godasi, Satyam. "Identification and control of non-linear distributed parameter systems /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2002. http://uclibs.org/PID/11984.
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Pearce-Lance, Jacob. "Methods for Parameter Identification in the Mitchell-Schaeffer Model." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39615.
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This thesis focusses on the development and testing of optimization methods for parameter identification in cardiac electrophysiology models. Cardiac electrophysiology models are systems of differential equations representing the evolution of the trans-membrane potential of cardiac cells. The Mitchell-Schaeffer model is chosen for this thesis. The parameters included in the Mitchell-Schaeffer model are optimally adjusted so that the solution of the model has desired properties. Two optimization problems are formulated using least-square functions to identify parameters that match phase durations and parameters that fit entire potential recordings of swine heart tissue acquired via optical imaging techniques at different stimulation frequencies. The non-differentiable optimization methods (Compass Search and three other variants) are applied to solving both optimization problems for two reasons; First, the methods are studied to evaluate performance and second, the optimization process is evaluated to confirm its ability to identify parameters for the Mitchell-Schaeffer model.
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Jen, Tsai Cheng, and 蔡政任. "System Parameter Identification of Arch DamsSystem Parameter Identification of Arch DamsSystem Parameter Identification of Arch DamsSystem Parameter Identification of Arch Dams." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/04266076686149678701.
Full textAbstract:
碩士國立中興大學
土木工程學系
93
The interaction of dam and reservoir is an important role on studying the dam-water system. The purpose of the paper is to develop the relationship between the frequency of dam-water system and the one of the dam only. From the relationship, we would know that there are several essential parameters to influence the frequency of the dam. For example, the height of water, the mode shape of the dam.In this paper, we set up a model to prove the relationship from this paper by simulating at first. At last, we used the real earthquake response measurements from Fei-Tsui arch dam, and use the system identification to get the frequency of dam-water system, and use the math relationship developed from this paper to get the frequency of the dam.
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Li, Jyun-Sian, and 李俊賢. "New Algorithms for Robust Parameter Identification and Time-Variant Parameter Identification." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/67780492563357408574.
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博士國立臺灣大學
機械工程學研究所
100
Two subjects of continuous-time parameter identification problems expressed in linear regression form are discussed in this thesis. One is the time-invariant parameter identification while subject to non-stochastic disturbances termed as the robust identification. The other is the time-variant parameter identification. In addition to the measurement stochastic noise, the output signal of a system is usually contaminated with the non-stochastic disturbances which are usually resulted from errors of measure devices, system unmodled dynamics or the process disturbances acting on the system. Most identifications considering the disturbance as a white noise will have biased estimates while subject to these kinds of disturbances. In the parameterization, one can lump all the disturbances into one disturbance term at the output expressed in linear regression form. We proposes one off-line approach and two on-line approaches to deal with this problem. In the off-line approach, the unknown disturbance will be approximately expanded by a finite Fourier cosine series with unknown coefficients. The unknown coefficients and the known basis functions will be augmented to the original parameter vector and the regressor respectively. With the expanded regressor, one can obtain the estimates of the expanded parameter vector by adopting the least-squares batch calculation. A necessary condition on persistent excitation of the expanded regressor is proposed too. In the first of the two on-line approaches, the estimation scheme is built under the structure of gradient algorithm. A compensation is made to reject the effect of the disturbance in the estimation error dynamics by designing a stabilized controller. In the design procedure, the averaging method is used for system approximation and the $H_{infty}$ frequency shaping methodology is utilized to synthesize the controller. The control signal will be able to track the disturbance signal and cancel it in the estimation error dynamics and that guarantees the convergence of the parameter estimation. In the second of the on-line approaches, an state-observer based estimator is constructed. To include the estimation of the disturbance into the estimation scheme, the system plant is augmented with the model of the proposed disturbance generating filter also termed as dynamics extension filter. The Kalman filter is adopted to perform the states estimation. Compared with the conventional internal model approach, the proposed method could be applied to a more general disturbance class. The three proposed approaches can identify both parameters and the disturbance simultaneously. The design procedures of the above two on-line approaches can be grafted to the time-variant parameter identification problem with some modifications. Special consideration will be addressed in the context. Keywords: Robust identification, Time-variant parameter identification, Disturbance identification, Kalman filter.
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Hwang, Chorng Lieh, and 黃崇烈. "Non-linear joints parameter identification." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/15969331483064658344.
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Rabinowitz, Basil P. "Adaptive control and parameter identification." Thesis, 2015. http://hdl.handle.net/10539/18046.
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The broad theory of adaptive control is introduced, with
m o t i v a t i o n for using such techniques. The two mos t popu l a r
techniques, the Model Re f er e n c e A d a ptive C o n t r o l l e r s (MRAC)
and the Self Tuning C o n t r o l l e r s (STC) are studied in more
d e t a i l .
The MRAC and the STC often lead to identical solutions.
The c on d i t i o n s for which these two techni q u e s are e q u i v a l e n t
are discussed.
P a r a m e t e r Adap t a ti o n A l go r i t h m s (PAA) are required by both
the MRA a n : the STC. For this reason the PAA is e x a m i ne d
in some det.ai . This is i n itiated by de r i v i ng an o f f - l i n e
lea; -squares PAA. This is then c o n v e r t e d into a r ec u r s i v e
on-l in e estimator. Using intuitive arguments, the various
choices of gain p a r a m e t e r as well as the v a r ia t i o n s of the
nasic form o f the a l g o r i t h m are discussed. This i n c l ud e s a
w a r n in g as to w here the p i tf a l l s of such a l g o r i t h m s may lie.
In order to examine the s t a b il i t y of these a lgorithms, the
H y p e r s t a b i l i t y theorem is introduced. This requires k n o w l e d g e
of the Popov i n e q ua l i t y and Stric t l y P o s itive Real (SPR)
functions. This is intro d u c ed initially using i n t u i t i v e
ene i g y concepts after which the r i g o r ou s m a t h e m a t i c a l
representa* ion is d e r i v e d .
The H y p e r s t a b i l i t y T h e o r e m is then used to exam i n e the
s t a b i l i t y condition for various forms of the PAA.
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ZHAO, WEI-HE, and 趙維和. "Parameter identification for load models." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/21018584644538844268.
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Green, Kary. "Optimal sensor placement for parameter identification." Thesis, 2007. http://hdl.handle.net/1911/20506.
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This thesis surveys methods for determining sensor locations which maximize the achievable accuracy in parameter estimation of differential equations. The approach considers the placement of sensors as an experimental design problem. The optimal sensor locations are found based on the so-called Fisher Information Matrix. After illustration of the general methodology, a particular algorithm is presented which finds optimal weights associated with a given set of potential sensor locations. Numerical results are provided to show that improvements in the accuracy of parameter estimates can be achieved using the ideas reviewed in this thesis.
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Lang, Wen-Jung, and 藍文榮. "Dynamic Parameter Identification of Isolation Sysytem." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/97667790897391029212.
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Huang, Wen-Kun, and 黃文奎. "Parameter identification of cylindrical Spool restrictor." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/bj38dr.
Full textAbstract:
碩士中原大學
機械工程研究所
102
In this paper, the experimental method of identifying the throttle throttle rod spring obedience coefficient parameters and discuss the work of the throttle pressure compensated hydrostatic bearing characteristics, experiments using hydrostatic bearing flat bench, by measuring throttle inlet pressure, outlet pressure and flow, using least square error partial derivative equations solved simultaneously to obtain a constant throttle slider displacement factor. Experimental test four restrictor rod, oil pressure test in four out of this circle have measured the throttle lever Data, into a round rod continuous flow restrictor equation, using the minimum error square method to analyze slip Constant throttle lever, the displacement factor, the oil differential pressure flow through a round rod throttle pressure caused by spring flow regulator to load changes, identify the optimal design.
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Chen, Tzu-Hsiang, and 陳子祥. "Parameter Identification of Variable Zoom Camera." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/10762814197240470807.
Full textAbstract:
碩士國立成功大學
航空太空工程學系碩博士班
94
In the process of using a digital camera to measure real word objects, the problems we face directly are radial lens distortion, unknown camera model parameters, and gauge of illumination.All those problems will affect the measurement results. When we want to use a digital camera to measure objects, the first step we need to do is to calibrate camera parameters. For an un-calibrated camera computer vision system, it couldn’t measure the real size or even the comparative size of objects. After completing camera calibration, we can obtain the parameters from calibration. With the object image coordinates, we can calculate the real size of the objects. In the traditional measurement of non-zoom camera calibration, the objects can’t be measured or with difficulties in it if the objects are out of the view or the objects are too small to see. We can overcome this problem with changing the focal length parameter. For the non-zoom camera calibration, we use the Zhang method as basis that he putted forth in 1999. In this dissert, we discuss the focal length parameter and systematize it in the calibration model. We separate the focal length parameter from the Zhang calibration camera model, and measure the object size in many different focal lengths. We will prove that it can improve the measurement error when we change the focal length. Finally, we discuss iris and the initial focal length effect to the measure error.
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Wong, Sze-Chung, and 黃思聰. "Modal Parameter Identification Through Simulated Evolution." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/73178867749938107646.
Full textAbstract:
碩士國立成功大學
航空太空工程學系
84
In this thesis, modal parameter identification of linear vibrating structure based on optimization approach is studied. A global optimization search algorithm based on the Darwinian Evolutionary Theory is implemented for identification of modal parameters of a linear vibrating structure. The capability of the evolutionary method in locating the global minimum among numerous local minima will be demonstrated. Moreover, different kinds of convergence criteria for the simulated evolution and their effects on the identification results will be studied. The reliability of the searching technique in modal parameter identification under different measurement noise levels will also be examined. Results show that the technique is effective and reliable in identifying dominant modes under moderate noisy conditions.
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Ruan, Weidong. "Modeling and parameter identification for rail systems /." 2005. http://wwwlib.umi.com/dissertations/fullcit/3189299.
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詹啟鋒. "Modal Parameter Identification Using Ambient Vibration Data." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/14139585133526994337.
Full textAbstract:
碩士國立成功大學
航空太空工程學系
88
Dynamical systems can be characterized by their modal parameters, which include natural frequencies, damping ratios and mode shapes. Identification of system characteristics is usually accomplished using both input and output data from the structural system. In many cases, however, only output measurements are available for structures under ambient conditions. It can be shown that if the input signals can be modeled as white noise the theoretical auto- and cross-correlation functions of structural response have the same mathematical form as free vibration of the structure. This thesis is considered modal parameter identification using ambient data. This is accomplished via adding, in cascade, a pseudo-force system to the structure’s system under consideration. The input to the pseudo-force system is white noise and the output of which is the actual force(s) applied to the structure. The structure’s responses solely are then used to identify the combined system. Structural parameters are then sorted out from identification result by using stability property of structural modes or orthogonality property of mode shapes.