A controller design method is presented that gives the best linear-quadratic-Gaussian closed-loop performance over a set of worst plant parameter changes. The design algorithm combines a multiplant optimal design code...
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A controller design method is presented that gives the best linear-quadratic-Gaussian closed-loop performance over a set of worst plant parameter changes. The design algorithm combines a multiplant optimal design code, SANDY, with a new worst parameter algorithm that uses a quadratic norm on parameter changes. The minimax algorithm is unique in the way it weights worst plants to expand the stable region in the parameter space. The method is applied to a two-mass/spring American Control Conference "benchmark" problem. A minimax controller is first designed for the case where the spring constant alone is uncertain. Next, several minimax controllers, including a reduced-order design, are synthesized for the benchmark problem where both masses and the spring constant are uncertain. The results show that minimax control provides near-optimal nominal performance with significant robustness and parameter margin improvements.
This paper presents the self-controlling software paradigm and reports on its use to control the branch and bound based constraint satisfaction problem solving algorithm. In this paradigm, an algorithm is first concep...
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This paper presents the self-controlling software paradigm and reports on its use to control the branch and bound based constraint satisfaction problem solving algorithm. In this paradigm, an algorithm is first conceptualized as a dynamical system and then a feedback control loop is added to control its behavior. The loop includes a Quality of Service component that assesses the performance of the algorithm during its run time and a controller that adjusts the parameters of the algorithm in order to achieve the control goal. Although other approaches - generally termed as "self-*" - make use of control loops, this use is limited to the structure of the software system, rather than to its behavior and its dynamics. This paper advocates the analysis of dynamics of any program with control loops. The self-controlling software paradigm is evaluated on two different NP-hard constraint satisfaction and optimization problems. The results of the evaluation show an improvement in the performance due to the added control loop for both of the tested constraint satisfaction problems. (C) 2012 Elsevier Inc. All rights reserved.
This paper describes a novel optimization-based approach to conflict resolution in air traffic control, based on geometric programming. A key feature of this approach is its ability to also take into account various m...
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This paper describes a novel optimization-based approach to conflict resolution in air traffic control, based on geometric programming. A key feature of this approach is its ability to also take into account various metering directives issued by the traffic flow management level, in contrast to most methods that focus purely on aircraft separation issues. Moreover, the proposed methodology can account for some of the nonlinearities present in the formulations of conflict resolution Problems, while incurring only a small penalty in computation time with respect to the fastest linear-programming-based approaches. Integer variables can be introduced to improve the quality of the solutions and to include combinatorial choices, for example, to optimize over aircraft sequences in merging streams. Simulation results demonstrate the efficiency of the approach on various aircraft separation problems, including miles-in-trail and minutes-in-trail restrictions through airspace fixes and boundaries.
In this paper, we will consider a modelization of Smart Grid on three sub-components: local level, microgrid level and T&D level. Thus and on one hand, we will propose an algorithm that manages its local level. Ou...
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In this paper, we will consider a modelization of Smart Grid on three sub-components: local level, microgrid level and T&D level. Thus and on one hand, we will propose an algorithm that manages its local level. Our algorithm is paired with branch and bound algorithm to solve knapsack problem. The main goal of this algorithm is to regulate consumption peaks and manage the priority of domestic appliances, by spreading at best the energy depending on the priority and consumption without exceeding the total energy received. Furthermore, we will introduce an asynchronous distributed Max-Flow algorithm to resolve the routing problem in order to optimize the T&D level. This algorithm uses a local computation to compute max-flow. Nodes communicate only by exchanging messages and no global information is needed. Our algorithm uses more than one augmenting path at each iteration allowing optimization of the execution time required for computing the max-flow.
In this work we study binary two-stage robust optimization problems with objective uncertainty. We present an algorithm to calculate efficiently lower bounds for the binary two-stage robust problem by solving alternat...
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In this work we study binary two-stage robust optimization problems with objective uncertainty. We present an algorithm to calculate efficiently lower bounds for the binary two-stage robust problem by solving alternately the underlying deterministic problem and an adversarial problem. For the deterministic problem any oracle can be used which returns an optimal solution for every possible scenario. We show that the latter lower bound can be implemented in a branch and bound procedure, where the branching is performed only over the first-stage decision variables. All results even hold for non-linear objective functions which are concave in the uncertain parameters. As an alternative solution method we apply a column-and-constraint generation algorithm to the binary two-stage robust problem with objective uncertainty. We test both algorithms on benchmark instances of the uncapacitated single-allocation hub-location problem and of the capital budgeting problem. Our results show that the branch and bound procedure outperforms the column-and-constraint generation algorithm.
This paper considers the problem of determinizing probabilistic data to enable such data to be stored in legacy systems that accept only deterministic input. Probabilistic data may be generated by automated data analy...
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This paper considers the problem of determinizing probabilistic data to enable such data to be stored in legacy systems that accept only deterministic input. Probabilistic data may be generated by automated data analysis/enrichment techniques such as entity resolution, information extraction, and speech processing. The legacy system may correspond to pre-existing web applications such as Flickr, Picasa, etc. The goal is to generate a deterministic representation of probabilistic data that optimizes the quality of the end-application built on deterministic data. We explore such a determinization problem in the context of two different data processing tasks-triggers and selection queries. We show that approaches such as thresholding or top-1 selection traditionally used for determinization lead to suboptimal performance for such applications. Instead, we develop a query-aware strategy and show its advantages over existing solutions through a comprehensive empirical evaluation over real and synthetic datasets.
This paper considers a simple assembly line balancing problem with fixed number of workstations and prespecified cycle time. Our objective is to minimize the sum of the squared deviations of the workstation loads arou...
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This paper considers a simple assembly line balancing problem with fixed number of workstations and prespecified cycle time. Our objective is to minimize the sum of the squared deviations of the workstation loads around the cycle time, hence maintain workload smoothing. We develop several optimality properties and bounding mechanisms, and use them in our branch and bound algorithm. The results of our computational study reveal that our branch and bound algorithm is capable of solving medium sized problem instances in reasonable times. (C) 2017 Published by Elsevier Ltd.
The intent of this Note is to show that the piecewise linear control allocation problem can be solved fast enough to be implemented in a digital flight-control system. The approach taken here differs from the authors&...
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The intent of this Note is to show that the piecewise linear control allocation problem can be solved fast enough to be implemented in a digital flight-control system. The approach taken here differs from the authors' initial paper on this subject in two ways. The first difference is a move away from the mixed-integer linear programming form of the optimization problem and to a linear programming formulation. The linear programming problem will be solved using a modified simplex algorithm where a rule-based approach is employed to enforce the necessary adjacency constraints on the interpolating coefficients. The second difference is that we solve a mixed optimization problem2 as opposed to the solution of the multibranch control allocation problem.' This allows us to achieve the same objective as before, but only having to solve one optimization problem instead of two. We will compare the performance of the simplex method with restricted basis entry rules to the mixed-integer formulation and show that the two approaches give equivalent solutions to the same set of control allocation problems. To perform this comparison, we will look at both closed-loop and open-loop control allocation problems. In the latter case, a set of control allocation problems are randomly selected and solved by each approach. For the former, we compare the algorithms in a digital simulation of a reentry vehicle on approach and landing.
This Note studies 3-D-resonant hopping, showing that the Tisserand constant is the main problem parameter. Some new analytical formulas and the 3-D Tisserand graph, which are the first main results, are developed to s...
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This Note studies 3-D-resonant hopping, showing that the Tisserand constant is the main problem parameter. Some new analytical formulas and the 3-D Tisserand graph, which are the first main results, are developed to support the design of resonant hopping transfers. The formulas are implemented in a branch-and-boundalgorithm for the computation of resonant hopping transfers. All the feasible solutions for a specific choice of boundary condition are computed in just a few minutes, despite the high dimensionality of the solution space, which grows exponentially with the number of gravity assists. The algorithm and the computation of the solution space are the second main results of the Note, and they are used for the trajectory design of JAXA's JMO.
This paper focuses on the problem of scheduling n independent jobs on m identical parallel machines for the objective of minimizing total tardiness of the jobs. We develop dominance properties and lower bounds, and de...
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This paper focuses on the problem of scheduling n independent jobs on m identical parallel machines for the objective of minimizing total tardiness of the jobs. We develop dominance properties and lower bounds, and develop a branch and bound algorithm using these properties and lower bounds as well as upper bounds obtained from a heuristic algorithm. Computational experiments are performed on randomly generated test problems and results show that the algorithm solves problems with moderate sizes in a reasonable amount of computation time. (c) 2005 Elsevier B.V. All rights reserved.
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