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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
81

Predictive Energy Management of Long-Haul Hybrid Trucks : Using Quadratic Programming and Branch-and-Bound

Jonsson Holm, Erik January 2021 (has links)
This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.
82

Block-decomposition and accelerated gradient methods for large-scale convex optimization

Ortiz Diaz, Camilo 08 June 2015 (has links)
In this thesis, we develop block-decomposition (BD) methods and variants of accelerated *9gradient methods for large-scale conic programming and convex optimization, respectively. The BD methods, discussed in the first two parts of this thesis, are inexact versions of proximal-point methods applied to two-block-structured inclusion problems. The adaptive accelerated methods, presented in the last part of this thesis, can be viewed as new variants of Nesterov's optimal method. In an effort to improve their practical performance, these methods incorporate important speed-up refinements motivated by theoretical iteration-complexity bounds and our observations from extensive numerical experiments. We provide several benchmarks on various important problem classes to demonstrate the efficiency of the proposed methods compared to the most competitive ones proposed earlier in the literature. In the first part of this thesis, we consider exact BD first-order methods for solving conic semidefinite programming (SDP) problems and the more general problem that minimizes the sum of a convex differentiable function with Lipschitz continuous gradient, and two other proper closed convex (possibly, nonsmooth) functions. More specifically, these problems are reformulated as two-block monotone inclusion problems and exact BD methods, namely the ones that solve both proximal subproblems exactly, are used to solve them. In addition to being able to solve standard form conic SDP problems, the latter approach is also able to directly solve specially structured non-standard form conic programming problems without the need to add additional variables and/or constraints to bring them into standard form. Several ingredients are introduced to speed-up the BD methods in their pure form such as: adaptive (aggressive) choices of stepsizes for performing the extragradient step; and dynamic updates of scaled inner products to balance the blocks. Finally, computational results on several classes of SDPs are presented showing that the exact BD methods outperform the three most competitive codes for solving large-scale conic semidefinite programming. In the second part of this thesis, we present an inexact BD first-order method for solving standard form conic SDP problems which avoids computations of exact projections onto the manifold defined by the affine constraints and, as a result, is able to handle extra large-scale SDP instances. In this BD method, while the proximal subproblem corresponding to the first block is solved exactly, the one corresponding to the second block is solved inexactly in order to avoid finding the exact solution of a linear system corresponding to the manifolds consisting of both the primal and dual affine feasibility constraints. Our implementation uses the conjugate gradient method applied to a reduced positive definite dual linear system to obtain inexact solutions of the latter augmented primal-dual linear system. In addition, the inexact BD method incorporates a new dynamic scaling scheme that uses two scaling factors to balance three inclusions comprising the optimality conditions of the conic SDP. Finally, we present computational results showing the efficiency of our method for solving various extra large SDP instances, several of which cannot be solved by other existing methods, including some with at least two million constraints and/or fifty million non-zero coefficients in the affine constraints. In the last part of this thesis, we consider an adaptive accelerated gradient method for a general class of convex optimization problems. More specifically, we present a new accelerated variant of Nesterov's optimal method in which certain acceleration parameters are adaptively (and aggressively) chosen so as to: preserve the theoretical iteration-complexity of the original method; and substantially improve its practical performance in comparison to the other existing variants. Computational results are presented to demonstrate that the proposed adaptive accelerated method performs quite well compared to other variants proposed earlier in the literature.
83

Métodos de programação quadrática convexa esparsa e suas aplicações em projeções em poliedros / Sparse convex quadratic programming methods and their applications in projections onto poliedra

Polo, Jeinny Maria Peralta 07 March 2013 (has links)
O problema de minimização com restrições lineares e importante, não apenas pelo problema em si, que surge em várias áreas, mas também por ser utilizado como subproblema para resolver problemas mais gerais de programação não-linear. GENLIN e um método eficiente para minimização com restrições lineares para problemas de pequeno e médio porte. Para que seja possível a implementação de um método similar para grande porte, é necessário ter um método eficiente, também para grande porte, para projeção de pontos no conjunto de restrições lineares. O problema de projeção em um conjunto de restrições lineares pode ser escrito como um problema de programação quadrática convexa. Neste trabalho, estudamos e implementamos métodos esparsos para resolução de problemas de programação quadrática convexa apenas com restrições de caixa, em particular o clássico método Moré-Toraldo e o \"método\" NQC. O método Moré-Toraldo usa o método dos Gradientes Conjugados para explorar a face da região factível definida pela iteração atual, e o método do Gradiente Projetado para mudar de face. O \"método\" NQC usa o método do Gradiente Espectral Projetado para definir em que face trabalhar, e o método de Newton para calcular o minimizador da quadrática reduzida a esta face. Utilizamos os métodos esparsos Moré-Toraldo e NQC para resolver o problema de projeção de GENLIN e comparamos seus desempenhos / The linearly constrained minimization problem is important, not only for the problem itself, that arises in several areas, but because it is used as a subproblem in order to solve more general nonlinear programming problems. GENLIN is an efficient method for solving small and medium scaled linearly constrained minimization problems. To implement a similar method to solve large scale problems, it is necessary to have an efficient method to solve sparse projection problems onto linear constraints. The problem of projecting a point onto a set of linear constraints can be written as a convex quadratic programming problem. In this work, we study and implement sparse methods to solve box constrained convex quadratic programming problems, in particular the classical Moré-Toraldo method and the NQC \"method\". The Moré-Toraldo method uses the Conjugate Gradient method to explore the face of the feasible region defined by the current iterate, and the Projected Gradient method to move to a different face. The NQC \"method\" uses the Spectral Projected Gradient method to define the face in which it is going to work, and the Newton method to calculate the minimizer of the quadratic function reduced to this face. We used the sparse methods Moré-Toraldo and NQC to solve the projection problem of GENLIN and we compared their performances
84

Sequential Quadratic Programming-Based Contingency Constrained Optimal Power Flow

Pajic, Slobodan 30 April 2003 (has links)
The focus of this thesis is formulation and development of a mathematical framework for the solution of the contingency constrained optimal power flow (OPF) based on sequential quadratic programming. The contingency constrained optimal power flow minimizes the total cost of a base case operating state as well as the expected cost of recovery from contingencies such as line or generation outages. The sequential quadratic programming (SCP) OPF formulation has been expanded in order to recognize contingency conditions and the problem is solved as a single entity by an efficient interior point method. The new formulation takes into account the system corrective capabilities in response to contingencies introduced through ramp-rate constraints. Contingency constrained OPF is a very challenging problem, because each contingency considered introduces a new problem as large as the base case problem. By proper system reduction and benefits of constraint relaxation (active set) methods, in which transmission constraints are not introduced until they are violated, the size of the system can be reduced significantly Therefore, restricting our attention to the active set constraint set makes this large problem significantly smaller and computationally feasible.
85

Planejamento ótimo de trajetórias para um robô escalador. / Optimal trajectory planning for a climbing robot.

Silva, Lucas Franco da 20 February 2018 (has links)
Este trabalho trata do planejamento de trajetórias que minimizam as perdas elétricas no KA\'I yxo, um robô escalador de árvores que tem por finalidade realizar monitoramento ambiental em florestas através da coleta de diferentes tipos de dados. Como essa aplicação requer que o robô permaneça em ambientes remotos, o estudo de técnicas que reduzam as perdas de energia a fim de que se aumente o tempo em operação do robô se mostra relevante, sendo a minimização das perdas elétricas uma contribuição importante nesse sentido. Estruturalmente, o KA\'I yxo consiste em um robô bípede com duas garras e quatro ligamentos interconectados por três juntas rotacionais. Além disso, seu mecanismo de andadura foi biologicamente inspirado na forma de locomoção observada em lagartas mede-palmos, o que permitiu tratar o robô como um manipulador industrial, cuja base é o ligamento associado à garra engastada e cujo efetuador é o ligamento associado à garra livre. Com isso, quando conveniente, o robô foi tratado em dois casos, conforme a garra que se encontra engastada. Inicialmente, realizou-se a modelagem matemática do robô, obtendo-se as equações cinemáticas direta e inversa, e dinâmicas, bem como o modelo das juntas segundo a abordagem do controle independente por junta. Posteriormente, formulou-se um problema de controle ótimo, solucionado através de um método numérico que o transformou em um problema de programação quadrática, que por sua vez foi resolvido iterativamente. Por fim, as trajetórias ótimas planejadas foram implementadas no robô real e, como forma de validação, as novas perdas elétricas foram comparadas com as das trajetórias anteriormente executadas pelo robô, determinando-se a correspondente economia de energia. / This work deals with the minimum-energy trajectory planning, related to the electrical losses, in KA\'I yxo, a tree-climbing robot that aims to perform environmental monitoring in forests through the collection of different types of data. As this application requires that the robot remains in remote environments, the study of techniques that reduce energy losses in order to increase the operation time of the robot is shown to be relevant, and the minimization of the electrical losses is an important contribution in this sense. Structurally, KA\'I yxo consists of a biped robot with two claws and four links interconnected by three revolute joints. In addition, its gait mechanism was biologically inspired in the form of locomotion observed in caterpillars, allowing to treat the robot as an industrial manipulator, which base is the link associated with the fixed claw and which end-effector is the link associated with the free claw. In consequence, when convenient, the robot was treated in two cases, according to the claw that is fixed. Initially, the mathematical model of the robot was developed, being obtained the forward and inverse kinematic and dynamic equations, as well as the model of the joints according to the independent joint control approach. Subsequently, an optimal control problem was formulated, which was solved through a numerical method that turned it into a quadratic programming problem, which in turn was solved iteratively. Finally, the planned optimal trajectories were implemented in the real robot and, as a form of validation, the new electrical losses were compared with those of the trajectories previously executed by the robot, being determined the corresponding energy saving.
86

Método previsor-corretor primal-dual de pontos interiores em problemas multiobjetivo de despacho econômico e ambiental /

Stanzani, Amélia de Lorena. January 2012 (has links)
Orientador: Antonio Roberto Balbo / Banca: Helenice de Oliveira F. Silva / Banca: Edmea Cassia Baptista / Resumo: O presente trabalho apresenta o método primal-dual previsor-corretor de pontos interiores para programação quadrática, com restrições lineares e quadráticos e variáveis canalizadas, e a aplicação deste método na resolução de problemas multiobjetivo de despacho econômico e ambiental, encontrados na engenharia elétrica. Pretende-se determinar soluções que sejam eficientes em relação ao custo dos combustíveis empregados na geração termoelétrica de energia e ao controle da emissão de poluentes, investigando-se duas estratégias: a primeira estratégica considera na função objetivo a soma ponderada entre as funções objetivo econômica e objetivo ambiental; a segunda estratégia considera o problema de despacho econômico condicionado à restrição ambiental, limitada superiormente para níveis permissíveis de missão. Para a resolução destes, uma implementação computacional do método primal-dual foi realizada em linguagem de programação C++, considerando o procedimento previsor-corretor com uma estratégia de barreira modificada para as restrições quadráticas de desigualdade, quando consideramos a segunda estratégia. Os resultados obtidos demonstram a eficiência do método em destaque em comparação a outros métodos como algoritmos genéticos co-evolutivo, atávico híbrido e cultural, bem como ao método primal-dual de pontos interiores, com procedimento de busca unidimensional, que estão divulgados na literatura / Abstract: This paper presents the primal-dual predictor-corrector interior point method for quadratic programming with linear and quadratic constraints and bounded variables, and its application in multiobjective problems of economic and environmental dispatch, found in electrical engineering. It is intended to determine effective solutions to the fuel cost used in thermal power generation and emissions control, by investigating two strategy; the first strategy considers the objective function as weighted sum of economic and environmental objective functions; the second strategy considers the economic dispatch problem subject to environmental constraint, upper bounded for allowable emission levels. To solve them, a computational implementation of primal-dual methods was performed in C++ programming language, considering the predictor-corrector procedure with a strategy of modified barrier for the quadratic inequality constraints, when we considerer the second strategy. The results obtained demonstrate the efficiency of the method highlighted in comparison with the co-evolutive genetic algorithms, hybrid and atavistic cultural, as well the primal-dual interior point method with one-dimensional search procedure, which are found in the literature / Mestre
87

Um método previsor-corretor primal-dual de pontos interiores barreira logarítmica modificada, com estratégias de convergência global e de ajuste cúbico, para problemas de programação não-linear e não-convexa /

Pinheiro, Ricardo Bento Nogueira. January 2012 (has links)
Orientador: Antonio Roberto Balbo / Banca: Edilaine Martins Soler / Banca: Leonardo Nepomuceno / Resumo: Neste trabalho apresentamos o método previsor-corretor primal-dual de pontos interiores, com barreira logarítmica modificada e estratégia de ajuste cúbico (MPIBLM-EX) e o método previsor-corretor primal-dual de pontos interiores, com barreira logarítmica modificada, com estratégias de ajuste cúbico e de convergência global (MPIBLMCG-EX). Na definição do algoritmo proposto, a função barreira logarítmica modificada auxilia o método em sua inicialização com pontos inviáveis. Porém, a inviabilidade pode ocorrer em pontos tais que o logaritmo não está definido, consequentemente, isso implica na não existência de função barreira logarítmica modificada. Para suprir essa dificuldade um polinômio cúbico ajustado ao logaritmo, que preserva as derivadas de primeira e segunda do mestre definido a partir de um ponto da região ampliada ao método previsor-corretor primal-dual de pontos interiores com barreira logarítmica modificada (MPIBML); no processo previsor são realizadas atualizações do parâmetro de barreira nos resíduos das restrições de complementaridade, considerando aproximações de primeira ordem do sistema de direções de busca, enquanto que no procedimento corretor, incluímos os termos quadráticos não-lineares dos resíduos citados, que foram desprezados no procedimento previsor. Considerando também a estratégia de convergência global para o MPIBLM-EX, a qual utiliza uma variante do método de Levenberg-Marquardt para ajustar a matriz dual normal da função lagrangiana, caso esta não seja definida positiva. A matriz dual normal é redefinida para as restrições primais de igualdade, de desigualdade e para as variáveis canalizadas, incorporando variáveis duais e matrizes diagonais relativas às restrições de complementariade. Desse estudo, o MPIBLM-EX é transformado no MPIBLMCG-EX e mostramos... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This work presents a predictor primal-dual interior point method with modified log-barrier and third order extrapolation strategy (IPMLBM-EX) and also and extension of this method with the inclusion of the global convergence strategy (IPMLBGCM-EX). In the definition of the proposed algorithm, the modified log-barrier function helps the method initialize with infeasible points. However, infeasibility may occur for some point where the logarithm is not defined. The implicates in non-existence of the modified log-barrier function. To cope with such as problem, a cubic polynomial function is adjusted to the logarithmic function. Sucha polynomial function preserves first and second order derivatives in certain point defined in the extended region. This function is applied to the predictor-corretor primal-dual interior point method with modified log-barrier function. In the predictor procedure, the barrier parameter is updated in the complementarity conditions considering first-order approximations of the search direction, while the corrector procedure includes the nonlinear quadratic terms of the mentioned residuals, which were neglected in the predictor procedure. We also consider the global convergence strategy for the method, which uses a variant of the Levenberg-Marquardt method to update the normal dual matrix of the Langrangian function, should it fail to be positively defined. In this case, this matrix is redefined for equality primal constraints, bounded inequality primal constraints and bounded variables, incorporating dual variables and diagonal matrices of the complementarity constraints. From such studies, the IPMLBM-EX method is extended to include the global convergence strategy (IPMLBGCM-EX). We have show that both methods are projected gradient methods. An implementation performed with Matlab 6.1 has shown the... (Complete abstract click electronic access below) / Mestre
88

Reliable Real-Time Optimization of Nonconvex Systems Described by Parametrized Partial Differential Equations

Oliveira, I.B., Patera, Anthony T. 01 1900 (has links)
The solution of a single optimization problem often requires computationally-demanding evaluations; this is especially true in optimal design of engineering components and systems described by partial differential equations. We present a technique for the rapid and reliable optimization of systems characterized by linear-functional outputs of partial differential equations with affine parameter dependence. The critical ingredients of the method are: (i) reduced-basis techniques for dimension reduction in computational requirements; (ii) an "off-line/on-line" computational decomposition for the rapid calculation of outputs of interest and respective sensitivities in the limit of many queries; (iii) a posteriori error bounds for rigorous uncertainty and feasibility control; (iv) Interior Point Methods (IPMs) for efficient solution of the optimization problem; and (v) a trust-region Sequential Quadratic Programming (SQP) interpretation of IPMs for treatment of possibly non-convex costs and constraints. / Singapore-MIT Alliance (SMA)
89

On Some Properties of Interior Methods for Optimization

Sporre, Göran January 2003 (has links)
This thesis consists of four independent papers concerningdifferent aspects of interior methods for optimization. Threeof the papers focus on theoretical aspects while the fourth oneconcerns some computational experiments. The systems of equations solved within an interior methodapplied to a convex quadratic program can be viewed as weightedlinear least-squares problems. In the first paper, it is shownthat the sequence of solutions to such problems is uniformlybounded. Further, boundedness of the solution to weightedlinear least-squares problems for more general classes ofweight matrices than the one in the convex quadraticprogramming application are obtained as a byproduct. In many linesearch interior methods for nonconvex nonlinearprogramming, the iterates can "falsely" converge to theboundary of the region defined by the inequality constraints insuch a way that the search directions do not converge to zero,but the step lengths do. In the sec ond paper, it is shown thatthe multiplier search directions then diverge. Furthermore, thedirection of divergence is characterized in terms of thegradients of the equality constraints along with theasymptotically active inequality constraints. The third paper gives a modification of the analytic centerproblem for the set of optimal solutions in linear semidefiniteprogramming. Unlike the normal analytic center problem, thesolution of the modified problem is the limit point of thecentral path, without any strict complementarity assumption.For the strict complementarity case, the modified problem isshown to coincide with the normal analytic center problem,which is known to give a correct characterization of the limitpoint of the central path in that case. The final paper describes of some computational experimentsconcerning possibilities of reusing previous information whensolving system of equations arising in interior methods forlinear programming. <b>Keywords:</b>Interior method, primal-dual interior method,linear programming, quadratic programming, nonlinearprogramming, semidefinite programming, weighted least-squaresproblems, central path. <b>Mathematics Subject Classification (2000):</b>Primary90C51, 90C22, 65F20, 90C26, 90C05; Secondary 65K05, 90C20,90C25, 90C30.
90

Model Predictive Control for Series-Parallel Plug-In Hybrid Electrical Vehicle

Engman, Jimmy January 2011 (has links)
The automotive industry is required to deal with increasingly stringent legislationfor greenhouse gases. Hybrid Electric Vehicles, HEV, are gaining acceptance as thefuture path of lower emissions and fuel consumption. The increased complexityof multiple prime movers demand more advanced control systems, where futuredriving conditions also becomes interesting. For a plug-in Hybrid Electric Vehicle,PIHEV, it is important to utilize the comparatively inexpensive electric energybefore the driving cycle is complete, this for minimize the cost of the driving cycle,since the battery in a PIHEV can be charged from the grid. A strategy with lengthinformation of the driving cycle from a global positioning system, GPS, couldreduce the cost of driving. This by starting to blend the electric energy with fuelearlier, a strategy called blended driving accomplish this by distribute the electricenergy, that is charged externally, with fuel over the driving cycle, and also ensurethat the battery’s minimum level reaches before the driving cycle is finished. Astrategy called Charge Depleting Charge Sustaining, CDCS, does not need lengthinformation. This strategy first depletes the battery to a minimum State of Charge,SOC, and after this engages the engine to maintain the SOC at this level. In thisthesis, a variable SOC reference is developed, which is dependent on knowledgeabout the cycle’s length and the current length the vehicle has driven in the cycle.With assistance of a variable SOC reference, is a blended strategy realized. Thisis used to minimize the cost of a driving cycle. A comparison between the blendedstrategy and the CDCS strategy was done, where the CDCS strategy uses a fixedSOC reference. During simulation is the usage of fuel minimized; and the blendedstrategy decreases the cost of the driving missions compared to the CDCS strategy.To solve the energy management problem is a model predictive control used. Thedesigned control system follows the driving cycles, is charge sustaining and solvesthe energy management problem during simulation. The system also handlesmoderate model errors. / Fordonsindustrin måste hantera allt strängare lagkrav mot utsläpp av emissioneroch växthusgaser. Hybridfordon har börjat betraktas som den framtida vägenför att ytterligare minska utsläpp och användning av fossila bränslen. Den ökadekomplexiteten från flera olika motorer kräver mera avancerade styrsystem. Begränsningarfrån motorernas energikällor gör att framtida förhållanden är viktigaatt estimera. För plug-in hybridfordon, PIHEV, är det viktigt att använda denvvijämförelsevis billiga elektriska energin innan fordonet har nått fram till slutdestinationen.Batteriets nuvarande energimängd mäts i dess State of Charge, SOC.Genom att utnyttja information om hur långt det är till slutdestinationen från ettGlobal Positioning System, GPS, blandar styrsystemet den elektriska energin medbränsle från början, detta kallas för blandad körning. En strategi som inte hartillgång till hur långt fordonet ska köras kallas Charge Depleting Charge Sustaining,CDCS. Denna strategi använder först energin från batteriet, för att sedanbörja använda förbränningsmotorn när SOC:s miniminivå har nåtts. Strategin attanvända GPS informationen är jämförd med en strategi som inte har tillgång tillinformation om körcykelns längd. Blandad körning använder en variabel SOC referens,till skillnad från CDCS strategin som använder sig av en konstant referenspå SOC:s miniminivå. Den variabla SOC referensen beror på hur långt fordonethar kört av den totala körsträckan, med hjälp av denna realiseras en blandad körning.Från simuleringarna visade det sig att blandad körning gav minskad kostnadför de simulerade körcyklerna jämfört med en CDCS strategi. En modellbaseradprediktionsreglering används för att lösa energifördelningsproblemet. Styrsystemetföljer körcykler och löser energifördelningsproblemet för de olika drivkällorna undersimuleringarna. Styrsystemet hanterar även måttliga modellfel.

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