Made available in DSpace on 2014-06-11T19:32:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-07-19Bitstream added on 2014-06-13T21:03:40Z : No. of bitstreams: 1 ramos_sb_me_jabo.pdf: 520535 bytes, checksum: 6d50ca4d7611871079ff37f8e15c9d68 (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Medidas periódicas de peso corporal de avestruzes (Struthio camelus) foram utilizadas para avaliar o desenvolvimento corporal por meio de modelos não lineares e para obter estimativas de herdabilidade para pesos ao nascer (PN), aos 90 (P90) e aos 180 dias de idade (P180). As funções estudadas foram: Brody, Richards, von Bertalanffy, Gompertz e Logístico, pelo método de Gauss Newton, por meio de regressão não-linear das características em função da idade (dias). As estimativas de herdabilidade foram obtidas pelo método de máxima verossimilhança restrita. Todos os modelos não-lineares ajustaram parâmetros para o crescimento corporal, exceto Richards e Brody. O modelo Gompertz foi o mais adequado para representar o crescimento, conforme a interpretação biológica dos parâmetros e a qualidade do ajuste. As estimativas de herdabilidade foram de 0,42±0,05, 0,16±0,04 e 0,24±0,08 para PN, P90 e P180, respectivamente. Todas as características poderão responder à seleção / Periodic measurements of body weight of ostriches (Struthio camelus) was used to evaluate the body development by nonlinear models and to fit heritability estimates of hatched chick weight (CHWT) and live weights recorded at 90 (LW90) and 180 (LW180) days of age. The studied models were: Brody, Richards, von Bertalanffy, Gompertz and Logistic, by Gauss-Newton method, on nonlinear regression of the traits in function of the age (days). The heritability estimates were obtained by restricted maximum likelihood method. All the nonlinear models fitted parameters for body growth, except Richards and Brody. The Gompertz model was the more appropriate to represent the growth, considering the biological interpretation of the parameters and the quality of the fitting. The heritability estimates were 0,42±0,05, 0,16±0,04 and 0,24±0,08 for CHWT, LW90 and LW180 respectively. All the traits can respond to selection

Orientador: Danísio Prado Munari / Banca: Maria Estela Gaglianone Moro / Banca:João Ademir de Oliveira / Resumo: Medidas periódicas de peso corporal de avestruzes (Struthio camelus) foram utilizadas para avaliar o desenvolvimento corporal por meio de modelos não lineares e para obter estimativas de herdabilidade para pesos ao nascer (PN), aos 90 (P90) e aos 180 dias de idade (P180). As funções estudadas foram: Brody, Richards, von Bertalanffy, Gompertz e Logístico, pelo método de Gauss Newton, por meio de regressão não-linear das características em função da idade (dias). As estimativas de herdabilidade foram obtidas pelo método de máxima verossimilhança restrita. Todos os modelos não-lineares ajustaram parâmetros para o crescimento corporal, exceto Richards e Brody. O modelo Gompertz foi o mais adequado para representar o crescimento, conforme a interpretação biológica dos parâmetros e a qualidade do ajuste. As estimativas de herdabilidade foram de 0,42±0,05, 0,16±0,04 e 0,24±0,08 para PN, P90 e P180, respectivamente. Todas as características poderão responder à seleção / Abstract: Periodic measurements of body weight of ostriches (Struthio camelus) was used to evaluate the body development by nonlinear models and to fit heritability estimates of hatched chick weight (CHWT) and live weights recorded at 90 (LW90) and 180 (LW180) days of age. The studied models were: Brody, Richards, von Bertalanffy, Gompertz and Logistic, by Gauss-Newton method, on nonlinear regression of the traits in function of the age (days). The heritability estimates were obtained by restricted maximum likelihood method. All the nonlinear models fitted parameters for body growth, except Richards and Brody. The Gompertz model was the more appropriate to represent the growth, considering the biological interpretation of the parameters and the quality of the fitting. The heritability estimates were 0,42±0,05, 0,16±0,04 and 0,24±0,08 for CHWT, LW90 and LW180 respectively. All the traits can respond to selection / Mestre

This thesis presents a robust nonlinear model predictive controller (RNMPC), nominal nonlinear model predictive controller (NMPC) and single-loop proportional-integral-derivative (PID) controllers that are applied to a nonlinear model of a run-of-mine (ROM) ore milling circuit. The model consists of nonlinear modules for the individual process units of the milling circuit (such as the mill, sump and cyclone), which allow arbitrary milling circuit configurations to be modelled easily. This study aims to cast a complex problem of a ROM ore milling circuit into an RNMPC framework without losing the flexibility of the modularised nonlinear model and implement the RNMPC using open-source software modules. The three controllers are compared in a simulations study to determine the performance of the controllers subject to severe disturbances and model parameter variations. The disturbances include changes to the feed ore hardness, changes in the feed ore size distributions and spillage water being added to the sump. The simulations show that the RNMPC and NMPC perform better than the PID controllers with regard to the economic objectives, assuming full-state feedback is available, especially when actuator constraints become active. The execution time of the RNMPC, however, is much too long for real-time implementation and would require further research to improve the efficiency of the implementation. / Thesis (PhD)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

abstract: Spatial regression is one of the central topics in spatial statistics. Based on the goals, interpretation or prediction, spatial regression models can be classified into two categories, linear mixed regression models and nonlinear regression models. This dissertation explored these models and their real world applications. New methods and models were proposed to overcome the challenges in practice. There are three major parts in the dissertation. In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution. In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine. A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data. / Dissertation/Thesis / Doctoral Dissertation Statistics 2020

Increased safety and productivity are driving factors for the trend in the mining industry where equipment and machines increasingly get automated. An example is the load-haul-dump vehicle, which is a machine that is used for transport of ore in underground mines. The cyclic load-haul-dump process is well suited for automation and automated loaders are commercially available today. Recent advances in autonomous driving have raised questions if there are efficiency gains that can be made by improving the path following algorithms that are used in the control. The aim of this thesis is to investigate the usage of model predictive control for path following for center-articulated mining vehicles. Two path following nonlinear model predictive controllers are designed and implemented. One controller is based on an error dynamics model, formulated as a regulation problem and implemented with the open source NMPC-library GRAMPC. The second controller is based on a kinematic model, formulated as a reference tracking NMPC problem and implemented using the embedded-MPC software tool FORCESPRO. The controllers are simulated on the same hardware that is used in real load-haul-dump vehicles, in a simulation environment provided by Epiroc Rock Drills AB. The results from the simulations show that both controllers can successfully follow a path, with a similar level of path error and less aggressive control actions compared to the current path following controller. The implemented controllers perform the control computations within a range of milliseconds on the embedded hardware, which is fast enough for real-time operation of the load-haul-dump vehicle.

This thesis focuses on the possibilities of application of nonlinear model predictive control for electric drives. Specifically, for drives with a permanent magnet synchronous motor. The thesis briefly describes the properties of this type of drive and presents its mathematical model. After that, a nonlinear model of predictive control and methods of nonlinear optimization, which form the basis for the controller output calculation, are described. As it is used in the proposed algorithm, the Active set method is described in more detail. The thesis also includes simulation experiments focusing on the choice of the objective function on the ability to control the drive. The same effect is examined for the different choices of the length of the prediction horizon. The end of the thesis is dedicated to the comparison between the proposed algorithm and commonly used field oriented control. The computational demands of the proposed algorithm are also measured and compared to the used sampling time.

Les travaux de recherche présentésdans cette thèse ont été effectués dans le cadred'une collaboration entre Leroy Somer et lelaboratoire de génie électrique et électronique deParis (GeePs). Ils ont pour objectif lessimulations des phénomènes observés en modetransitoire des machines électriques. Cessimulations sont particulièrement orientées parles nouvelles exigences issues du Grid Code pourles alternateurs connectés au réseau.Principalement, deux types de modèles ont étédéveloppés. Le premier se base sur unereprésentation de l’état magnétique de lamachine où chaque flux est exprimé en fonctiondes courants des différentes bobines. Le secondmodèle regroupe les courants en utilisant descourants magnétisants sur les axes d et q associésà des coefficients de saturation pour chaque fluxet simplifie la représentation magnétique,notamment pour la prise encompte du circuit amortisseur. Avec unemodélisation suffisamment précise ducomportement magnétique non linéaire de lamachine, ils permettent de mieux prédire lescourants et le couple électromagnétique lors desdéfauts tels que les creux de tension. Les travauxeffectués présentés dans ce mémoire ont permis,en partant des descriptions des saturationstrouvées dans une machine, de définir desméthodes pour incorporer la saturation dans lesmodèles de type circuit et finalement d’aboutirau choix du modèle non-linéaire pour unemachine électrique donnée. Grâce à un temps decalcul réduit, ils ont aussi conduit à l'intégrationsous Simulink de modèles de la machine et dusystème d'entrainement pour la réalisationd'études de stabilité et pour créer unenvironnement de mise au point de la commandedu système. / The research presented in this thesiswas carried out in the research and developmentproject between Leroy Somer and the Group ofElectrical Engineering of Paris (GeePs). Theirobjective is to simulate the phenomena observedin the transient states of electrical machines.These simulations are particularly oriented bythe new Grid Code requirements for alternatorsconnected to the power network. Two types ofmodels have been principally developed. Thefirst one is based on a magnetic description ofthe machine where each flux is expressed as afunction of the currents flowing through thedifferent machine windings. The second oneregroups the different winding currents by usingthe magnetizing currents on axes d and qassociated to saturation coefficients for eachflux linkage and simplifies the magneticdescription, especially when taking into accountthe damper windings. With a sufficiently precisemodelling of the non-linear magnetic behaviourof the machine, it is possible to better predict thecurrents and the electromagnetic torque underfault conditions such as voltage drops. The workcarried out in this thesis has made possible,starting from the descriptions of the saturationeffects found in a machine, to define methodsfor incorporating saturation into circuit models.Finally, one can make a choice of the dynamicnon-linear model for a given machine. Thanks toshort computation time, it also led to theSimulink integration of the machine andexcitation system models paving the way forstability and control studies.

Les dernières années ont permis des développements très rapides, tant au niveau de l’élaboration que de l’application, d’algorithmes de commande prédictive non linéaire (CPNL), avec une gamme relativement large de réalisations industrielles. Un des obstacles les plus significatifs rencontré lors du développement de cette commande est lié aux incertitudes sur le modèle du système. Dans ce contexte, l’objectif principal de cette thèse est la conception de lois de commande prédictives non linéaires robustes vis-à-vis des incertitudes sur le modèle. Classiquement, cette synthèse peut s’obtenir via la résolution d’un problème d’optimisation min-max. L’idée est alors de minimiser l’erreur de suivi de la trajectoire optimale pour la pire réalisation d'incertitudes possible. Cependant, cette formulation de la commande prédictive robuste induit une complexité qui peut être élevée ainsi qu’une charge de calcul importante, notamment dans le cas de systèmes multivariables, avec un nombre de paramètres incertains élevé. Pour y remédier, une approche proposée dans ces travaux consiste à simplifier le problème d’optimisation min-max, via l’analyse de sensibilité du modèle vis-à-vis de ses paramètres afin d’en réduire le temps de calcul. Dans un premier temps, le critère est linéarisé autour des valeurs nominales des paramètres du modèle. Les variables d’optimisation sont soit les commandes du système soit l’incrément de commande sur l’horizon temporel. Le problème d’optimisation initial est alors transformé soit en un problème convexe, soit en un problème de minimisation unidimensionnel, en fonction des contraintes imposées sur les états et les commandes. Une analyse de la stabilité du système en boucle fermée est également proposée. En dernier lieu, une structure de commande hiérarchisée combinant la commande prédictive robuste linéarisée et une commande par mode glissant intégral est développée afin d’éliminer toute erreur statique en suivi de trajectoire de référence. L'ensemble des stratégies proposées est appliqué à deux cas d'études de commande de bioréacteurs de culture de microorganismes. / The last few years have led to very rapid developments, both in the formulation and the application of Nonlinear Model Predictive Control (NMPC) algorithms, with a relatively wide range of industrial achievements. One of the most significant challenges encountered during the development of this control law is due to uncertainties in the model of the system. In this context, the thesis addresses the design of NMPC control laws robust towards model uncertainties. Usually, the above design can be achieved through solving a min-max optimization problem. In this case, the idea is to minimize the tracking error for the worst possible uncertainty realization. However, this robust approach tends to become too complex to be solved numerically online, especially in the case of multivariable systems with a large number of uncertain parameters. To address this shortfall, the proposed approach consists in simplifying the min-max optimization problem through a sensitivity analysis of the model with respect to its parameters, in order to reduce the calculation time. First, the criterion is linearized around the model parameters nominal values. The optimization variables are either the system control inputs or the control increments over the prediction horizon. The initial optimization problem is then converted either into a convex optimization problem, or a one-dimensional minimization problem, depending on the nature of the constraints on the states and commands. The stability analysis of the closed-loop system is also addressed. Finally, a hierarchical control strategy is developed, that combines a robust model predictive control law with an integral sliding mode controller, in order to cancel any tracking error. The proposed approaches are applied through two case studies to the control of microorganisms culture in bioreactors.

The first goal of this work was to develop models based on nonlinear ordinary-differential equations or nonlinear algebraic equations, which produce the lift and drag coefficients on a cylinder or a cylinder-like structure. We introduced an improved wake oscillator for the lift, which combines the van der Pol and Duffing equations. We proposed a two-term quadratic model that relates the drag and lift coefficients, which reproduces the phase relationship between the drag and lift and its variation with the Reynolds number. We found that a mixed-type (external and parametric) forcing is needed to represent the effects of the cylinder motion. The second goal of this work was to develop a deeper understanding of the shedding and fluid forces on a cylinder and how they depend on its oscillatory motion within and outside the synchronization (or lock-in) band of frequencies. We performed extensive CFD (computational fluid dynamics) simulations and solved the unsteady Reynolds-averaged Navier-Stokes equations that govern the flow fields around fixed and moving (in either the cross-flow or in-line direction) cylinders. We identified various wake modes that can exist, depending on the cylinder motion (direction, amplitude, and frequency) by using modern methods of nonlinear dynamics. The possible responses can be period-one, periodic with large period, quasiperiodic, or chaotic. Moreover, we found that the route to chaos is torus breakdown. We investigated how four frequency sweeps of the cross-flow motion affect the response curves and the hysteresis phenomenon. We studied in detail the effect of the in-line motion on the wake and related this effect to the reduction in the lift and mean drag due to a synchronization type that is very different from the one due to cross-flow motion. / Ph. D.

Silicon carbide is a very promising semiconductor material for high-power, highrequency, and high-temperature applications. SiC distinguishes from traditional narrow bandgap semiconductors, such as silicon, in that common doping impurities in SiC have activation energies larger than the thermal energy kT even at room temperature. This causes incomplete ionization of such impurities, which leads to strong temperature and frequency dependence of the semiconductor junction differential admittance and, if carrier freeze-out effects are not taken into account, errors in doping profiles calculated from capacitance-voltage data. Approaches commonly used to study the influence of incomplete impurity ionization on the junction admittance are based on the truncated space charge approximation and/or the small-signal approximation. The former leads to impurity ionization time constant and occupation number errors, while the latter fails if the measurement ac signal amplitude is larger than kT/q. In this work, a new reverse bias Schottky junction admittance model valid for the general case of an arbitrary temperature, measurement signal frequency and amplitude, and doping occupation number and time constant distributions is developed. Results of junction admittance calculations using the developed model are compared with the results of traditional models. Based on the general model, a new method of admittance spectroscopy data analysis is created and used to determine impurity parameters more accurately than allowed by traditional approaches. Incomplete impurity ionization is investigated for the case of nitrogen donors and aluminum and boron acceptors in 4H- and 6H-SiC. It is shown that the degree of carrier freeze-out is significant in heavily N-doped 6H-SiC and in Al- and B-doped SiC. Frequency dispersion of the junction admittance is shown to be significant at room temperature in N- and B-doped SiC. Junction capacitance calculations as a function of applied dc bias show that calculated doping profiles deviate from the actual impurity concentration profiles if the impurity ionization time constant is comparable with the ac signal period. This is the case for N- and B-doped SiC with certain values of the impurity activation energy and capture cross-section. Validity of the new model and its predictions are successfully tested on experimental admittance data for N- and B-doped SiC Schottky diodes.