Dissertations / Theses on the topic 'Linear parameter'
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1
Hernandez, Erika Lyn. "Parameter Estimation in Linear-Linear Segmented Regression." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3551.pdf.
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Ollikainen, Kati. "PARAMETER ESTIMATION IN LINEAR REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4138.
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Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist with confidence intervals not including the true parameter (even though the variable was selected). The Elder/Pregibon rule was used for variable selection. A comparison of the bootstrap Percentile and BCa confidence interval was made as well as an investigation of adjustments to the usual 95% confidence intervals based on the Bonferroni and Scheffe multiple comparison principles. The results show that linear regression has problems in capturing the true parameters in the confidence intervals for the sample sizes considered, the bootstrap intervals perform no better than linear regression, and the Scheffe method is too wide for any application considered. The Bonferroni adjustment is recommended for larger sample sizes and when the t-value for a selected variable is about 3.35 or higher. For smaller sample sizes all methods show problems with type II errors resulting from confidence intervals being too wide.Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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Zhang, Xiping. "Parameter-Dependent Lyapunov Functions and Stability Analysis of Linear Parameter-Dependent Dynamical Systems." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5270.
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The purpose of this thesis is to develop new stability conditions for several linear dynamic systems, including linear parameter-varying (LPV), time-delay systems (LPVTD), slow LPV
systems, and parameter-dependent linear time invariant (LTI) systems. These stability conditions are less conservative and/or computationally easier to apply than existing ones.
This dissertation is composed of four parts. In the first part of this thesis, the complete stability domain for LTI parameter-dependent (LTIPD) systems is synthesized by extending existing results in the literature. This domain is calculated through a guardian map which involves the determinant of the Kronecker sum of a matrix with itself. The stability domain is
synthesized for both single- and multi-parameter dependent LTI systems. The single-parameter case is easily computable, whereas the multi-parameter case is more involved. The determinant of the
bialternate sum of a matrix with itself is also exploited to reduce the computational complexity.
In the second part of the thesis, a class of parameter-dependent Lyapunov functions is proposed, which can be used to assess the stability properties of single-parameter LTIPD systems in a non-conservative manner. It is shown
that stability of LTIPD systems is equivalent to the existence of a Lyapunov function of a polynomial type (in terms of the parameter) of known, bounded degree satisfying two matrix inequalities. The bound of polynomial degree of the Lyapunov functions is then reduced by taking advantage of the fact that the Lyapunov matrices are symmetric. If the matrix multiplying the parameter is not full rank, the polynomial order
can be reduced even further. It is also shown that checking the feasibility of these matrix
inequalities over a compact set can be cast as a convex optimization problem. Such Lyapunov functions and stability conditions for affine single-parameter LTIPD systems are then generalized to single-parameter polynomially-dependent LTIPD systems and affine multi-parameter LTIPD systems.
The third part of the thesis provides one of the first attempts to derive computationally tractable criteria for analyzing the stability of LPV time-delayed systems. It presents both
delay-independent and delay-dependent stability conditions, which are derived using appropriately selected Lyapunov-Krasovskii functionals. According to the system parameter dependence, these functionals can be selected to obtain increasingly non-conservative results. Gridding techniques may be used to cast these tests as Linear Matrix Inequalities (LMI's). In cases when
the system matrices depend affinely or quadratically on the parameter, gridding may be avoided. These LMI's can be solved efficiently using available software. A numerical example of a
time-delayed system motivated by a metal removal process is used to demonstrate the theoretical results.
In the last part of the thesis, topics for future
investigation are proposed. Among the most interesting avenues for research in this context, it is proposed to extend the existing stability analysis results to controller synthesis, which will be based on the same Lyapunov functions used
to derive the nonconservative stability conditions. While designing the dynamic ontroller for linear and parameter-dependent systems, it is desired to take the advantage of the rank deficiency of the system matrix multiplying the parameter such that the controller is of lower dimension, or rank deficient without sacrificing the performance of closed-loop systems.
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Wang, Feng. "Robust control of quasi-linear parameter-varying L2 point formation flying with uncertain parameters." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/6933.
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Robust high precision control of spacecraft formation flying is one of the most important techniques required for high-resolution interferometry missions in the complex deep-space environment. The thesis is focussed on the design of an invariant stringent performance controller for the Sun-Earth L2 point formation flying system over a wide range of conditions while maintaining system robust stability in the presence of parametric uncertainties. A Quasi-Linear Parameter-Varying (QLPV) model, generated without approximation from the exact nonlinear model, is developed in this study. With this QLPV form, the model preserves the transparency of linear controller design while reflecting the nonlinearity of the system dynamics. The Polynomial Eigenstructure Assignment (PEA) approach used for Linear Time-Invariant (LTI) and Linear Parameter-Varying (LPV ) models is extended to use the QLPV model to perform a form of dynamic inversion for a broader class of nonlinear systems which guarantees specific system performance. The resulting approach is applied to the formation flying QLPV model to design a PEA controller which ensures that the closed-loop performance is independent of the operating point. Due to variation in system parameters, the performance of most closed-loop systems are subject to model uncertainties. This leads naturally to the need to assess the robust stability of nonlinear and uncertain systems. This thesis presents two approaches to this problem, in the first approach, a polynomial matrix method to analyse the robustness of Multiple-Input and Multiple-Output (MIMO) systems for an intersectingD-region,which can copewith time-invariant uncertain systems is developed. In the second approach, an affine parameterdependent Lyapunov function based Linear Matrix Inequality (LMI) condition is developed to check the robust D-stability of QLPV uncertain systems.
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Macatula, Romcholo Yulo. "Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99411.
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We consider uncertainty quantification using surrogate Gaussian processes. We take a previous sampling algorithm and provide a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of the resulting posterior distributions. We test methods on 1D deconvolution and 2D tomography. Our new methods improve on the previous algorithm, however fall short in some aspects to a typical Bayesian inference method.Master of Science
Parameter uncertainty quantification seeks to determine both estimates and uncertainty regarding estimates of model parameters. Example of model parameters can include physical properties such as density, growth rates, or even deblurred images. Previous work has shown that replacing data with a surrogate model can provide promising estimates with low uncertainty. We extend the previous methods in the specific field of linear models. Theoretical results are tested on simulated computed tomography problems.
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Carter, Lance Huntington. "Linear parameter varying representations for nonlinear control design /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
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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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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 relative degree are required, except that the system transfer functions must be strictly proper.
Under standard gaussian assumptions on the various noises, for time-invariant systems in the class described above, it is proved that PLID is the optimal estimator (in the mean-square·error sense) of the states and the parameters, conditioned on the output measurements. It is also proved, under a reasonable assumption of persistent excitation, that the PLID parameter estimates converge a.e. to the true parameter values of the unknown system.
For deterministic systems, it is proved that PLID exactly identifies the states and parameters in the minimum possible time, so—called deadbeat identification. The proof brings out an interesting relation between the estimate error propagation and the observability matrix of the time-varying extended system (the extended system incorporates the unknown parameters into the state vector). This relation gives rise to an intuitively
appealing notion of persistent excitation.
Some results of system identification simulations are presented. Several different cases are simulated, including a two-input, two-output system with non-minimum-phase zeros, and an unstable system. A comparison of PLID with the widely used extended Kalman filter is presented for a single-input, single·output system with near cancellation of a pole-zero pair.
Results are also presented from simulations of the adaptive control of an unstable. two-input, two-output system In these simulations, PLID is used in a se1f—tuning regulator to identify the parameters needed to compute the feedback gain matrix, and (simultaneously) to estimate the system states, for the state feedbackPh. D.
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Kayhan, Belgin. "Parameter Estimation In Generalized Partial Linear Modelswith Tikhanov Regularization." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612530/index.pdf.
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Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the entire GPLM. Such a particular representation of submodels provides both
a better accuracy and a better stability (regularity) under noise in the data.
We aim to smooth the nonparametric part of GPLM by using a modified form of Multiple Adaptive Regression Spline (MARS) which is very useful for high-dimensional problems and does not impose any specific relationship between the predictor and
dependent variables. Instead, it can estimate the contribution of the basis functions so that both the additive and interaction effects of the predictors are allowed to determine
the dependent variable. The MARS algorithm has two steps: the forward and backward stepwise algorithms. In the rst one, the model is built by adding basis functions until a maximum level of complexity is reached. On the other hand, the backward stepwise algorithm starts with removing the least significant basis functions from the model.
In this study, we propose to use a penalized residual sum of squares (PRSS) instead of the backward stepwise algorithm and construct PRSS for MARS as a Tikhonov regularization problem. Besides, we provide numeric example with two data setsone has interaction and the other one does not have. As well as studying the regularization of the nonparametric part, we also mention theoretically the regularization of the parametric part. Furthermore, we make a comparison between Infinite Kernel Learning (IKL) and Tikhonov regularization by using two data sets, with the difference consisting in the (non-)homogeneity of the data set. The thesis concludes with an outlook on future research.
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BruyeÌ€re, Lilian Henri AndreÌ. "Robust parametric autopilot for quasi-linear parameter-varying missile." Thesis, Cranfield University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413400.
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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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Fitzpatrick, Kristin Lee. "Applications of linear parameter-varying control for aerospace systems." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0001820.
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Kaufmann, Stefan-Harald. "Does the parameter represent a fundamental concept of linear algebra?" Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-80339.
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In mathematics the parameter is used as a special kind of a variable. The classification of the terms \"variable\" and \"parameter\" is often done by intuition and changes due to different situations and needs. The history of mathematics shows that these two terms represent the same abstract object in mathematics. In today´s mathematics, compared to variables, the parameter is declared as an unknown
constant measure. This interpretation of parameters can be used in set theory for describing sets with an infinite number of elements. Due to this perspective the structure of vector spaces can be developed as a special structured set theory. Further, the concept of parameters can be seen as a model for developing mathematics education in linear algebra.
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Xiong, Dapeng. "Stability analysis and controller synthesis of linear parameter varying systems /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
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Boonto, Sudchai [Verfasser]. "Identification of Linear Parameter-Varying Input-Output Models / Sudchai Boonto." München : Verlag Dr. Hut, 2011. http://d-nb.info/1013526511/34.
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Zandi, Nia Arman. "Switching linear parameter-varying electronic throttle control for automotive engines." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/55165.
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The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate
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Alipourazadi, Shahram. "New approaches to linear graph modeling of distributed-parameter systems." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/41896.
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Analytical modeling is an important fundamental step in the development of procedures such as simulation, design, control, and health monitoring of engineering systems. Typically, physical properties such as inertia, flexibility (or stiffness), capacitance, inductance, and energy dissipation (mechanical damping or electrical resistance) are spatially distributed in a physical dynamic system. Often in dynamic models, these characteristics are approximated by spatially “lumped” elements. For better accuracy, however, the true distributed nature of these parameters has to be incorporated into the model. Distributed parameter (DP) models are important in this context. This thesis concerns the representation of distributed parameter engineering systems using linear graphs (LG).
Among possible approaches for modeling of engineering systems, linear graphs are used in the present work due to its numerous advantages as discussed in the thesis. An engineering system may possess physical properties in many domains such as mechanical, electrical, thermal, and fluid. Mechatronic systems are multi-domain systems, which typically possess at least electro-mechanical characteristics. Linear graphs present a domain-independent unified approach for modeling multi-domain systems. Furthermore, linear graphs have beneficial features in the development of automatic procedures for modeling and designing engineering systems, which are long-term goals of the present work.
In this thesis, approaches are developed for the representation of distributed-parameter systems as LG models. Different approaches are presented for this purpose and compared. The LG modeling approach enables one to visualize the system structure before formulating the dynamic equations of the system. For example, for a DP system the structure of its LG model may possess a well-defined pattern. In this work, vector linear graphs are introduced to take advantage of these patterns. General notations and elements are defined for vector linear graphs. As a result of this development a new single element is generated for use in the modeling of distributed-parameter systems, particularly in the mechanical domain.
In this thesis, a software toolbox is enhanced and presented for LG modeling, which is able to automatically extract the state space equations of a mechatronic system. This software tool is provided free for academic use and is accessible through the Internet. Throughout the thesis many comprehensive examples are provided to illustrate the developed concepts and procedures and their application.
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Chen, Jianchi. "Robust linear parameter varying control of an unmanned aerial vehicle." Thesis, University of Leicester, 2010. http://hdl.handle.net/2381/7930.
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The dynamic response characteristics of modern unmanned aerial vehicles (UAVs) are highly nonlinear and vary substantially with flight conditions due to their reduced dimensions compared to normal aircraft. In this thesis, design frameworks that are based on parameter dependent Lyapunov functions (PDLFs) are developed for UAV flight control systems. These design frameworks or procedures can systematically deal with aircraft systems with nonlinear and parameter dependent dynamics, and uncertainty in the mathematical models. To this end, we analyse robust stability and performance of LPV systems and present two LPV controller design methods using the PDLF approach: Two-Degree-of-Freedom (2DoF) and loop shaping with coprime factorisation. We formulate and solve the control problem for an LPV plant with measurable parameters and an output feedback structure. The solvability conditions are reduced to LMIs and can be solved approximately using finite-dimensional convex programming. A parameter dependent performance approach is used in a 2DoF/PDLF design and constitutes a flexible generalisation for calibrations of local performance. In loop shaping/PDLF design, a left coprime factorisation is derived by H2 filtering, and then a loop shaping design is implemented in the PDLF framework. We also incorporate pole placement constraints into the LMI synthesis to improve controller performance. To be able to use the robust gain-scheduling synthesis results, an LPV model of the UAV is developed and validated. The gain scheduling controller design of longitudinal/lateral-directional dynamics of the UAV is illustrated in the design example. It is shown that a flight control system can be built with satisfactory robust stability and performance.
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Goldberg, Sophia Rachel. "Two-parameter perturbation theory for cosmologies with non-linear structure." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/43168.
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We propose and construct a two-parameter expansion around a Friedmann-Lemaitre- Robertson-Walker geometry which uses both large-scale and small-scale perturbations analogous to cosmological perturbation theory and post-Newtonian gravity. We justify this observationally, derive a set of field equations valid on a fraction of the horizon size and perform a detailed investigation of the associated gauge problem. We find only the Newtonian gauge, out of the standard gauges used in cosmological perturbation theory, is applicable to post-Newtonian perturbations; we can identify a consistent set of perturbed quantities in the matter and gravity sectors and construct corresponding gauge-invariant quantities. The field equations, written in terms of these quantities, takes on a simpler form, and allows the effects of small-scale structure on the large-scale properties of the Universe to be clearly identified and discussed for different physical scenarios. With a definition of statistical homogeneity, we find that the cosmological constant and the average energy density, of radiation and dust, source the Friedmann equation, whereas only the inhomogeneous part of the Newtonian energy density sources the Newton-Poisson equation { even though both originate from the same equation. There exists field equations at new orders in our formalism, such as a frame-dragging field equation a hundred times larger than expected from using cosmological perturbation theory alone. Moreover, we find non-linear gravity, mode-mixing and a mixing-of-scales at orders one would not expect from intuition based on cosmological perturbation theory. By recasting the field equations as an effective fluid we observe that these non-linearities lead to, for example, a large-scale effective pressure and anisotropic stress. We expect our formalism to be useful for accurately modelling our Universe, and for investigating the effects of non-linear gravity in the era of ultra-large-scale surveys.
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Zhang, Lan. "Parameter identification in linear and nonlinear parabolic partial differential equations." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/37762.
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Mui, Glen B. Carleton University Dissertation Engineering Mechanical and Aerospace. "Parameter identification of a non-linear electrorheological fluid damper model." Ottawa, 1996.
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Liu, Danping. "Semiparametric methods in generalized linear models for estimating population size and fatality rate." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B36164598.
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Celik, Gul. "Parameter Estimation In Generalized Partial Linear Models With Conic Quadratic Programming." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612531/index.pdf.
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In statistics, regression analysis is a technique, used to understand and model the
relationship between a dependent variable and one or more independent variables.
Multiple Adaptive Regression Spline (MARS) is a form of regression analysis. It is a
non-parametric regression technique and can be seen as an extension of linear models
that automatically models non-linearities and interactions. MARS is very important
in both classification and regression, with an increasing number of applications in
many areas of science, economy and technology.
In our study, we analyzed Generalized Partial Linear Models (GPLMs), which are
particular semiparametric models. GPLMs separate input variables into two parts
and additively integrates classical linear models with nonlinear model part. In order
to smooth this nonparametric part, we use Conic Multiple Adaptive Regression Spline
(CMARS), which is a modified form of MARS. MARS is very benefical for high
dimensional problems and does not require any particular class of relationship between
the regressor variables and outcome variable of interest. This technique offers a great advantage for fitting nonlinear multivariate functions. Also, the contribution of the
basis functions can be estimated by MARS, so that both the additive and interaction
effects of the regressors are allowed to determine the dependent variable. There are
two steps in the MARS algorithm: the forward and backward stepwise algorithms. In
the first step, the model is constructed by adding basis functions until a maximum
level of complexity is reached. Conversely, in the second step, the backward stepwise
algorithm reduces the complexity by throwing the least significant basis functions from
the model.
In this thesis, we suggest not using backward stepwise algorithm, instead, we employ
a Penalized Residual Sum of Squares (PRSS). We construct PRSS for MARS as a
Tikhonov Regularization Problem. We treat this problem using continuous optimization
techniques which we consider to become an important complementary technology
and alternative to the concept of the backward stepwise algorithm. Especially, we apply
the elegant framework of Conic Quadratic Programming (CQP) an area of convex
optimization that is very well-structured, hereby, resembling linear programming and,
therefore, permitting the use of interior point methods.
At the end of this study, we compare CQP with Tikhonov Regularization problem
for two different data sets, which are with and without interaction effects. Moreover,
by using two another data sets, we make a comparison between CMARS and two
other classification methods which are Infinite Kernel Learning (IKL) and Tikhonov
Regularization whose results are obtained from the thesis, which is on progress.
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Pedchote, Chamnarn. "Parameter estimation for non-linear systems : an application to vehicle dynamics." Thesis, Cranfield University, 2003. http://dspace.lib.cranfield.ac.uk/handle/1826/3896.
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This work presents an investigation into the parameter estimation of suspension components and the vertical motions of wheeled vehicles from experimental data. The estimation problems considered were for suspension dampers, a single wheel station and a full vehicle. Using conventional methods (gradient-based (GB), Downhill Simplex (DS)) and stochastic methods (Genetic Algorithm (GA) and Differential Evolution (DE)), three major problems were encountered. These were concerned with the ability and consistency of finding the global optimum solution, time consumption in the estimation process, and the difficulties in setting the algorithm's control parameters. To overcome these problems, a new technique named the discrete variable Hybrid Differential Evolution (dvHDE) method is presented. The new dvHDE method employs an integer-encoding technique and treats all parameters involved in the same unified way as discrete variables, and embeds two mechanisms that can be used to deal with convergence difficulties and reduce the time consumed in the optimisation process. The dvHDE algorithm has been validated against the conventional GB, DS and DE techniques and was shown to be more efficient and effective in all but the simplest cases. Its robustness was demonstrated by its application to a number of vehicle related problems of increasing complexity. These include case studies involving parameter estimation using experimental data from tests on automotive dampers, a single wheel station and a full vehicle. The investigation has shown that the proposed dvHDE method, when compared to the other methods, was the best for finding the global optimum solutions in a short time. It is recommended for nonlinear vehicle suspension models and other similar systems.
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Bu, Yiming. "Switching linear parameter-varying control of a variable-speed wind turbine." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1596455.
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For variable-speed wind energy conversion systems, control objectives may be different in partial and full load regions (or in low and high wind speed regions). Typical control objectives are to maximize the energy capture in low wind speeds, and to maintain the generated power and the rotational turbine speed within safety limits during high wind speeds. In such a case, it is difficult to design a single robust controller covering both partial load and full load conditions. This paper presents a systematic switching control method for a variable-speed variable-pitch wind turbine over a wide wind speed region. The whole framework is based on the linear parameter-varying (LPV) control theory, which is an extension of robust control for linear systems to nonlinear ones.
Two LPV controllers are designed, each suitable in a different wind speed region. A hysteresis switching logic is applied to guarantee the stability when the switching event occurs between the two controllers. Nonlinear simulations are conducted to demonstrate the proposed control scheme.
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Herzog, Christian [Verfasser]. "Linear Parameter-Varying Control of Systems of High Complexity / Christian Hoffmann." München : Verlag Dr. Hut, 2016. http://d-nb.info/1106592980/34.
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Chng, Eng Siong. "Applications of nonlinear filters with the linear-in-the-parameter structure." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/16964.
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In particular, the Volterra and the radical basis function (RBF) expansion techniques are considered to implement the nonlinear filter structures. These approaches, however, will generate filters with very large numbers of parameters. As large filter models require significant implementation complexity, they are undesirable for practical implementations. To reduce the size of the filter, the orthogonal least squares (OLS) algorithm is considered to perform model selection. Simulations were conducted to study the effectiveness of subset models found using this algorithm, and the results indicate that this selection technique is adequate for many practical applications. The other aspect of the OLS algorithm studied is its implementation requirements. Although the OLS algorithm is very efficient, the required computational complexity is still substantial. To reduce the processing requirement, some fast OLS methods are examined. Two major applications of nonlinear filters are considered in the thesis. The first involves the use of nonlinear filters to predict time series with possesses nonlinear dynamics. To study the performance of the nonlinear predictors, simulations were conduced to compare the performance of these predictors with conventional linear predictors. The simulation results confirm that nonlinear predictors normally perform better than linear predictors. Within its study, the application of RBF predictors to time series that exhibit homogeneous nonstationarity is also considered. This type of time series possesses the same characteristic throughout the time sequence apart from local variations of mean and trend. The second application involves the use of filters for symbol-decision channel equalisation. The decision function of the optimal symbol-decision equaliser is first derived to show that it is nonlinear, and that it may be realised explicitly using a RBF filter. Analysis is then carried out to illustrate the difference between the optimum equaliser's performance and that of the conventional linear equaliser. In particular, the effects of delay order on the equaliser's decision boundaries and bit error rate (BER) performance are studied. The minimum mean square error (MMSE) optimisation criterion for training the linear equaliser is also examined to illustrate the sub-optimum nature of such a criterion. To improve the linear equaliser's performance, a method which adapts the equaliser by minimising the BER is proposed. Our results indicate that the linear equalisers performance is normally improved by using the minimum BER criterion. The decision feedback equaliser (DFE) is also examined. We propose a transformation using the feedback inputs to change the DFE problem to a feedforward equaliser problem. This unifies the treatment of the equaliser structures with and without decision feedback.
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Pedchote, C. "Parameter estimation for non-linear systems: an application to vehicle dynamics." Thesis, Engineering Systems Department, 2009. http://hdl.handle.net/1826/3896.
Full textAbstract:
This work presents an investigation into the parameter estimation of suspension
components and the vertical motions of wheeled vehicles from experimental data. The
estimation problems considered were for suspension dampers, a single wheel station and
a full vehicle.
Using conventional methods (gradient-based (GB), Downhill Simplex (DS)) and
stochastic methods (Genetic Algorithm (GA) and Differential Evolution (DE)), three
major problems were encountered. These were concerned with the ability and consistency
of finding the global optimum solution, time consumption in the estimation process, and
the difficulties in setting the algorithm's control parameters. To overcome these
problems, a new technique named the discrete variable Hybrid Differential Evolution
(dvHDE) method is presented.
The new dvHDE method employs an integer-encoding technique and treats all
parameters involved in the same unified way as discrete variables, and embeds two
mechanisms that can be used to deal with convergence difficulties and reduce the time
consumed in the optimisation process. The dvHDE algorithm has been validated against
the conventional GB, DS and DE techniques and was shown to be more efficient and
effective in all but the simplest cases. Its robustness was demonstrated by its application
to a number of vehicle related problems of increasing complexity. These include case
studies involving parameter estimation using experimental data from tests on automotive
dampers, a single wheel station and a full vehicle. The investigation has shown that the
proposed dvHDE method, when compared to the other methods, was the best for finding
the global optimum solutions in a short time. It is recommended for nonlinear vehicle
suspension models and other similar systems.
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Melo, Félix Eduardo Mapurunga de. "Modeling and linear parameter-varying identification of a two-tank system." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/25566.
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MELO, F. E. M. Modeling and linear parameter-varying identification of a two-tank system. 2017. 82 f. Dissertação (Mestrado em Engenharia Elétrica)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2017.Submitted by Marlene Sousa (mmarlene@ufc.br) on 2017-09-06T17:29:24Z No. of bitstreams: 1 2017_dis_femmelo.pdf: 6250467 bytes, checksum: 7ec83fc628244552eb74ae6b40c6fbc0 (MD5)
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This work addresses the modeling and the linear parameter-varying (LPV) system identification of a coupled two-tank system (TTS). The system is a multiple input multiple output (MIMO) with two inputs and two outputs. In order to obtain a suitable model for this system, a first-principle approach based on the mass balance principle is followed. It turns out that the modeling process was driven by the geometrical shape of the tanks. Thus, most of its parameters are based on the tanks’ dimensions. When it comes to the LPV identification, several methods are presented ranging from the classical results from the regression approach to the current support vector machines (SVM) based methods. All the identification algorithms presented are extended in order to cope with the MIMO systems. Additionally, a method based on instrumental variables support vector machines was adapted from the general nonlinear case to the LPV case. A new LPV model with two independent scheduling variables is proposed driven by prior knowledge on the process model. The results obtained with this new LPV model have showed a good performance in describing the TTS behavior. Furthermore, they were better than an LPV model considering only a single scheduling variable.
Este trabalho lida com a modelagem e identificação com abordagem de sistemas com parâmetros variantes (LPV) de um sistema de dois tanques acoplados (TTS). Esse sistema é do tipo múltipla entrada múltipla saída (MIMO) com duas entradas e duas saídas. Com a finalidade de obter um modelo adequado para esse sistema, é feita uma abordagem fenomenológica baseada no princípio do balanço de massa. Descobre-se que o processo de modelagem é dependente da forma geométrica dos tanques. Assim, a maioria dos seus parâmetros são baseados nas dimensões dos tanques. Quando se trata de identificação de sistemas LPV, vários métodos são apresentados desde os resultados clássicos baseados em regressão até os métodos atuais baseados em máquinas de vetor de suporte. Todos os algoritmos de identificação apresentados são estendidos para lidar com sistemas MIMO. Além disso, um método baseado em variáveis instrumentais com máquinas de vetor de suporte foi adaptado do caso não linear geral para o caso LPV. Um novo modelo LPV com duas variáveis de scheduling é proposto baseado em conhecimento a priori no modelo do processo. Os resultados obtidos com esse novo modelo LPV mostraram bom desempenho ao descrever o comportamento do sistema de dois tanques. Ademais, eles foram melhores do que um modelo LPV considerando apenas uma variável de scheduling.
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Besson, Vincent. "Parameter space robust control for S.I. engine idle speed." Thesis, University of Liverpool, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366369.
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Mekaiel, Mohammed M. "Bootstrap methods and parameter estimation in time series threshold modelling." Thesis, University of Salford, 1995. http://usir.salford.ac.uk/42975/.
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The aim of this thesis is to investigate of bootstrap methods (Efron, 1979), in the the performance estimation of parameter estimates in non-linear time series models, in particular SETAR models (Tong, 1993). First and higher order SETAR models in known and unknown thresholds cases are considered. To assess the performance of bootstrap methods, we first give an extensive simulation study (by using simulated normal errors), in chapters 3 and 4, to investigate large and small sample behaviours of the true sampling distributions of parameter estimates of SETAR models and how they are affected by sample size. First and higher order SETAR models in the known and unknown threshold cases are considered. An introduction to the bootstrap methods (Efron, 1979 ) is given in chapter 5. The effect of sample size on the bootstrap distributions of parameter estimates of first and higher order SETAR models in the known and unknown threshold cases ( for given order, delay and number of thresholds ) are also investigated in this chapter, via simulation and by using the same models used in the simulated normal errors 'true distribution' case ( chapters 3 & 4). The results are compared with simulated normal case in order to assess the bootstrap results. Tong and Lim (1980) method is used for fitting SETAR models to bootstrap samples, which is also used in the initial fit. Moreover, applications of bootstrap to celebrated data sets, namely, the logarithmically transformed lynx data covering the period (182-1934); and the sunspot numbers covering the period (1700- 1920), are attempted. The cyclical behaviours of bootstrap models are also examined. Finally, in chapter 5, an attempt is also made to study the problem of non-linear properties of the skeleton of a non-linear autoregressive process (Jones, 1976) via simulation and we study in particular a limit cycle behaviour.
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KOWTA, SRINIVAS. "ROBUST STABILITY ANALYSIS OF SERVO-HYDRAULIC SYSTEM IN PARAMETER SPACE." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1060970575.
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Kaiser, Gerd [Verfasser]. "Torque Vectoring - Linear Parameter-Varying Control for an Electric Vehicle / Gerd Kaiser." München : Verlag Dr. Hut, 2015. http://d-nb.info/1070124168/34.
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Genç, Ali Umut. "Linear parameter-varying modelling and robust control of variable cam timing engines." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619775.
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Childers, Adam Fletcher. "Parameter Identification and the Design of Experiments for Continuous Non-Linear Dynamical Systems." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28236.
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Mathematical models are useful for simulation, design, analysis, control, and optimization of complex systems. One important step necessary to create an effective model is designing an experiment from which the unknown model parameter can be accurately identified and then verified. The strategy which one approaches this problem is dependent on the amount of data that can be collected and the assumptions made about the behavior of the error in the statistical model. In this presentation we describe how to approach this problem using a combination of statistical and mathematical theory with reliable computation. More specifically, we present a new approach to bounded error parameter validation that approximates the membership set by solving an inverse problem rather than using the standard forward interval analysis methods. For our method we provide theoretical justification, apply this technique to several examples, and describe how it relates to designing experiments. We also address how to define infinite dimensional designs that can be used to create designs of any finite dimension. In general, finding a good design for an experiment requires a careful investigation of all available information and we provide an effective approach to dthe problem.Ph. D.
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Vugrin, Eric D. "On Approximation and Optimal Control of Nonnormal Distributed Parameter Systems." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11149.
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For more than 100 years, the Navier-Stokes equations and various linearizations have been used as a model to study fluid dynamics. Recently, attention has been directed toward studying the nonnormality of linearized problems and developing convergent numerical schemes for simulation of these sytems. Numerical schemes for optimal control problems often require additional properties that may not be necessary for simulation; these properties can be critical when studying nonnormal problems. This research is concerned with approximating infinite dimensional optimal control problems with nonnormal system operators.
We examine three different finite element methods for a specific convection-diffusion equation and prove convergence of the infinitesimal generators. Additionally, for two of these schemes, we prove convergence of the associated feedback gains. We apply these three schemes to control problems and compare the performance of all three methods.Ph. D.
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Pislaru, Crinela. "Parameter identification and hybrid mathematical modelling techniques applied to non-linear control systems." Thesis, University of Huddersfield, 2001. http://eprints.hud.ac.uk/id/eprint/4616/.
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The study is concerned with the mathematical modelling and parameter identification of nonlinear motion control systems. The control systems under review are the axis motion control systems for a 3-axis CNC machine tool. The modelling process commences with lumped parameter methods applied to each element and then to each axis in turn. Comparing the dynamic performance of the simulated axis drive with the machine tool axis performance identifies the need for a modular load and the introduction of measured non-linear effects. In order to achieve a realistic dynamic performance, a hybrid model incorporating a distributed load, explicit damping factors and measured non-linear effects was developed. The influence of factors like damping coefficients, the moving mass, velocity and positional gains, time constants of the servo control system on the servo characteristics of machine tool has been determined. Different friction models have been investigated and an effective simulation method has been developed for simulation purposes. This model represents to a large extent the dynamic behaviour of the drives from the actual machine, a fact proven by the comparison between the simulated and measured results. For multi-axis control, measured geometric errors at the machine were needed to be included in the model in order to achieve realistic contouring performance. The models were validated by comparison with measurements at the machine. A feature of this work is the developed measurement methods for parameter identification. This together with the definition of the stimuli provides a solid base for a universal identification process for application to non-linear machine tool axis drives. A novel wavelet algorithm for the determination of damping coefficients for CNC machine tools is introduced and this together with other developed parameter adjustment routines allows for the performance to be optimised.
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Masih, Hanifzadegan. "Linear parameter-varying control of CNC machine tool feed-drives with dynamic variations." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/51892.
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This thesis presents new approaches to feed-drive control of computer numerical control (CNC) machine tools machine tools with a significant range of dynamic variations during machining operations.
Several sources which can cause dynamic variations of feed-drive systems are considered, such as the change of table position, the reduction of workpiece mass, and the variations of tool-path orientation. Feed-drive systems having the dynamic variations are modeled as linear parameter varying (LPV) models. For the LPV models, three control methods are proposed to achieve satisfactory control performance of feed-drive systems.
In the first method, we propose a parallel structure of an LPV gain-scheduled controller which aims at both tracking control and the vibration suppression by taking into account the resonant modes' variations which are peculiar to ball-screw drives. In the second method, instead of designing one LPV controller, a set of gain-scheduled controllers are designed to compensate for a wide range of dynamic variations. In this method, switching between two adjacent controllers may result in a transient jump of control signal at switching instants.
In the third method, to ensure a smooth control signal, we present a novel method to design a smooth switching gain-scheduled LPV controller. The moving region of the gain-scheduling variables is divided into a specified number of local subregions as well as subregions for the smooth controller switching. Then, one gain-scheduled LPV controller is assigned to each of the local subregions, while for each switching subregion, a function interpolating local LPV controllers associated with its neighbourhood subregions is designed. This interpolating function imposes the constraint of smooth transition on controller system matrices.
The smooth switching controller design problem amounts to solving a feasibility problem which involves non-linear matrix inequalities that are solvable by a proposed iterative descent algorithm. The developed smooth switching controller is applied to control problems in both parallel and serial CNC machine tool mechanisms.
Finally, for the multi-axis CNC machine tools, a multi-input-multi-output (MIMO) LPV feedback controller is designed to directly minimize contouring error in the task coordinate frame system.Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate
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Suárez, Garcés María Elena [Verfasser], and Andreas [Akademischer Betreuer] Potschka. "Iterative Linear Algebra for Parameter Estimation / María Elena Suárez Garcés ; Betreuer: Andreas Potschka." Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177149486/34.
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Roberts, Gareth James. "Monitoring land cover dynamics using linear kernel-driven BRDF model parameter temporal trajectories." Thesis, University College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407145.
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Patterson, Byron Wain. "A linear parameter varying control methodology for reduction of helicopter higher harmonic vibration." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105564.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 155-159).
Vibration in helicopters can have a significant impact on their utility, maintenance, weight, performance, and cost of operation. This thesis focuses on the design of a vibration reduction controller that is effective throughout the flight envelope of the helicopter, in spite of variations in vibration levels and dynamics with flight condition. Analysis of a UH-60 Black Hawk helicopter modeled in the Rotorcraft Comprehensive Analysis System (RCAS) aeromechanical simulation environment indicates that the steady state vibration levels and the helicopter dynamics depend primarily on the advance ratio. Two baseline vibration controllers are developed, specifically the Continuous Time Higher Harmonic Controller (CTHHC) and an H[subscript of infinity] based controller, over a range of advance ratios. The unique controller developed in this thesis uses the Linear Parameter Varying (LPV) synthesis method, which provides performance and stability guarantees over the advance ratio parameter space. The three controllers are evaluated in the RCAS environment at fixed and maneuvering flight conditions. The results indicate that the full envelope controller designed using the LPV method exhibits increased performance over the CTHHC and H[subscript of infinity] controllers.
by Byron Wain Patterson.
S.M.
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Xue, Xiangming. "Development of a nonlinear wind turbine simulator for linear parameter-varying control design." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1603750.
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It is challenging to design a controller for a wind turbine system because of its nonlinear time-varying dynamics. To resolve this issue, linear parameter-varying (LPV) control theory has been applied in the past 10years to deal with nonlinear and wind-speed-dependent dynamics. Before applying the LPV control method, it is required to transform the nonlinear model of the system to an LPV model. The main objectives of this research are to model a 5-megawatt (MW) Reference Wind Turbine for Offshore System as an LPV system, develop a nonlinear wind turbine simulator using MATLAB/Simulink to validate the LPV model, and conduct a preliminary study on LPV control design to test the simulator, which will be used in the future research for applying different LPV control techniques, such as switching LPV control, LPV anti-windup control, and others.
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Sar, Preeti. "Eco-Inspired Robust Control Design Algorithm For Linear Systems with Real Parameter Uncertainty." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1367439491.
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Krak, Michael David. "Step-response of discontinuous non-linear torsional systems: Experimental and parameter estimation studies." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1460981012.
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Zebiri, Hossni. "Hinf-Linear Parameter Varying Controllers Order Reduction : Application to semi-active suspension control." Thesis, Mulhouse, 2016. http://www.theses.fr/2016MULH7733/document.
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L'amélioration permanente de la qualité et des performances des systèmes automatiques constitue un défi majeur dans la théorie du contrôle. La théorieHinf a permis d'améliorer considérablement les performances des correcteurs. Ces derniers reposent sur des modèles mathématiques qui sont potentiellement d'ordre élevé (c.-à-d. comprenant un nombre élevé d'équations différentielles). De plus, l'ajout de poids de pondérations spécifiant les performances à respecter accroit encore plus leur ordre. La complexité algorithmique résultante peut alors rendre leur implantation difficile voire même impossible pour un fonctionnement en temps réel.Les travaux présentés visent à réduire l'ordre de correcteurs Hinf dans le but de faciliter leur intégration tout en respectant les performances imposées d'une part et proposent une majoration de l'erreur introduite par l'étape de réduction d'autre part.Dans la littérature, de nombreuses méthodes pour la réduction d'ordre de modèles et de correcteurs des systèmes LTI ont été développées. Ces techniques ont été étudiées, comparées et testées sur un ensemble de benchmarks. S'appuyant sur ces travaux, nous proposons une extension aux systèmes linéaires à paramètres variants (LPV). Pour valider leurs performances, une application sur une commande d'une suspension semi-active a montré l'efficacité des algorithmes de réduction développésThe work presented in this dissertation is related to the Hinf-LPV-controller orderReduction. This latter consists of the design of a robust reduced-order LPV-controller for LPV-systems. The order reduction issue has been very fairly investigated. However, the case of LPV-control design is slightly discussed. This thesis focuses primarily on two topics: How to obtain an LPV-reduced-order controller even the high order generated by the classical synthesis and how this reduced order controller can deal with a practical engineering problem (semi-active suspension control). In view of this, the order-reduction topic and the Hinf-synthesis theory have been widely studied in this thesis. This study, has allowed the development of a new method forH1-LPV-controller order reduction
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Reis, Sandra Santos dos 1983. "Diagnóstico em modelos de regressão linear e não-linear com erros simétricos." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306148.
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Orientador: Mauricio Enrique Zevallos HerenciaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica
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Resumo: Neste trabalho discutimos a detecção de observações influentes em modelos simétricos lineares e não lineares. Em primeiro lugar é realizado um estudo de simulação para avaliar o desempenho de três métodos de estimação em dados gerados por quatro situações: sem observações influentes, com outliers na variável resposta, com observações influentes de média alavancagem e com observações influentes de alta alavancagem. São analisados dois métodos de máxima verossimilhança e um método robusto. Foram considerados modelos de regressão linear e não linear com erros logísticos tipo II e t-Student. Em segundo lugar é discutida detecção de observações influentes mediante a distância de Cook generalizada, a estatística de Peña e a estatística de Andrews-Pregibon. Em particular é discutida a conveniência de utilizar a metodologia de limiares para caracterizar uma observação como influente ou não influente, assim como o efeito da estimação de parâmetros na construção de limiares. Estas medidas foram aplicadas a conjuntos de dados reais e simulados considerando o ajuste de alguns modelos simétricos com uma adaptação no método de estimação scoring de Fisher
Abstract: We discuss the detection of influential observations in symmetrical linear and nonlinear regression models. First a simulation study is conducted to evaluate the performance of three estimation methods on data generated by four situations: without influential observations with outliers in the response variable, with influential observations average leverage and influential observations with high leverage. Two methods of maximum likelihood and robust method are analyzed. We considered linear and nonlinear regression models with logistic-II and Student-t errors. Secondly detection of influential observations by generalized Cook's distance, the statistic PeÃ?a and Andrews - Pregibon statistic is discussed. In particular the convenience of using the methodology to characterize a threshold observation as influential or not influential, as well as the effect of parameter estimation in the construction of thresholds is discussed. These measures were applied to sets of real and simulated data considering the fit of some symmetrical regression models with an adaptation estimation method of Fisher scoring
Mestrado
Estatistica
Mestra em Estatística
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Liu, Danping, and 劉丹平. "Semiparametric methods in generalized linear models for estimating population size and fatality rate." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B36164598.
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Javadian, Moein. "A numerical optimization approach to switching surface design for switching linear parameter-varying control." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46064.
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This thesis proposes an algorithm to design
switching surfaces for the switching linear parameter-varying
(LPV) controller with hysteresis switching. The switching surfaces
are sought for to optimize the bound of the closed-loop
L2-gain performance. An optimization problem is formulated
with respect to parameters characterizing Lyapunov matrix
variables, local controller matrix variables, and locations of
the switching surfaces. Since the problem turns out to be
non-convex in terms of these characterizing parameters, a
numerical algorithm is given to guarantee the decrease of the
cost function value after each iteration, which consists of two steps: direction selection and line search. A hybrid method which
is a combination of the steepest descent method and Newton's
method is employed in the direction selection step to decide the orientation of proceeding. A numerical algorithm is used to compute the most appropriate length of the proceeding along the selected direction which generates the most decrease in the cost function.
To demonstrate the efficiency and usefulness of the proposed algorithm, it will be applied to three examples in control applications: a tracking problem for a mass-spring-damper system, a vibration suppression problem for a magnetically-actuated optical image stabilizer, and an air-fuel-ratio control problem for automotive engines. In these examples, it will be shown that the proposed optimization approach to the design of the switching surfaces and the switching LPV controller is superior to heuristic approaches in closed-loop performances, at the price of higher computational costs. Additionally, it will be shown that the algorithm can be applied to the general n-parameters case.
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Theis, Julian [Verfasser], and Herbert [Akademischer Betreuer] Werner. "Robust and linear parameter-varying control of aeroservoelastic systems / Julian Theis ; Betreuer: Herbert Werner." Hamburg : Universitätsbibliothek der Technischen Universität Hamburg-Harburg, 2018. http://d-nb.info/1159954968/34.
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Herzog, Christian [Verfasser], and Herbert [Akademischer Betreuer] Werner. "Linear parameter-varying control of systems of high complexity / Christian Hoffmann. Betreuer: Herbert Werner." Hamburg : Universitätsbibliothek der Technischen Universität Hamburg-Harburg, 2016. http://d-nb.info/1103023365/34.
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