Limits on the storage space or the computation time restrict the applicability of model predictive controllers (MPC) in many real problems. Currently available methods either compute the optimal controller online or d...
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Limits on the storage space or the computation time restrict the applicability of model predictive controllers (MPC) in many real problems. Currently available methods either compute the optimal controller online or derive an explicit control law. In this paper we introduce a new approach combining the two paradigms of explicit and online MPC to overcome their individual limitations. The algorithm computes a piecewise affine approximation of the optimal solution that is used to warm-start an active set linearprogramming procedure. A preprocessing method is introduced that provides hard real-time execution, stability and performance guarantees for the proposed controller. By choosing a combination of the quality of the approximation and the number of online active set iterations the presented procedure offers a tradeoff between the warm-start and online computational effort. We show how the problem of identifying the optimal combination for a given set of requirements on online computation time, storage and performance can be solved. Finally, we demonstrate the potential of the proposed warm-start procedure on numerical examples.
This study intends to develop easy-to-implement and effective approaches to help decision makers cope with the challenges of designing their optimal systems, and determine the corresponding optimal budgets. The approa...
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This study intends to develop easy-to-implement and effective approaches to help decision makers cope with the challenges of designing their optimal systems, and determine the corresponding optimal budgets. The approaches are realized by constructing optimal system design (OSD) data envelopment analysis (DEA) models that build on the concepts and techniques of DEA, de novo programming and parametric linear programming with a parametric right-hand side. The proposed models explicitly take the important issue of congestion in system design into account. Therefore, the OSD DEA models can help decision makers not only optimally design their systems, but also determine their optimal budgets. Numerical examples are used to evaluate the strengths of the proposed models. (C) 2011 Elsevier Ltd. All rights reserved.
This paper discusses properties of the graphs of 2-way and 3-way transportation polytopes, in particular, their possible numbers of vertices and their diameters. Our main results include a quadratic bound on the diame...
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This paper discusses properties of the graphs of 2-way and 3-way transportation polytopes, in particular, their possible numbers of vertices and their diameters. Our main results include a quadratic bound on the diameter of axial 3-way transportation polytopes and a catalogue of non-degenerate transportation polytopes of small sizes. The. catalogue disproves five conjectures about these polyhedra stated in the monograph by Yemelichev et al. (1984). It also allowed us to discover some new results. For example, we prove that the number of vertices of an in x n transportation polytope is a multiple of the greatest common divisor of in and n. (C) 2009 Elsevier Inc. All rights reserved.
Constrained finite time optimal control problems can be expressed as mathematical programs parameterized by the current state of the system:the so-called multi-parametric *** problems have received a great deal of att...
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ISBN:
(纸本)9787811243901
Constrained finite time optimal control problems can be expressed as mathematical programs parameterized by the current state of the system:the so-called multi-parametric *** problems have received a great deal of attention in the control community during the last few years because solving the parametric program is equivalent to synthesizing the optimal state-feedback *** many cases of interest,the resulting synthesized controllers are simple piecewise-affine functions,which enables receding horizon control to be used not only in slowly sampled systems requiring powerful computers but now also in high-speed embedded *** primary limitation of these optimal’explicit solutions’is that the complexity can grow quickly with problem *** this talk I will introduce new methods to compute approximate explicit and online control laws that can trade-off time and space complexity against sub-optimality while providing guarantees of stability and feasibility.
parametricprogramming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, re...
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parametricprogramming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, resulting in a significant decrease in on-line computation effort. In this paper we survey recent work on parametric linear programming (pLP) from the point of view of the control engineer. We identify three types of algorithms, two arising from standard convex hull paradigms and one from a geometric intuition, and classify all currently proposed methods tinder these headings. Through this classification, we identify a third standard convex hull approach that offers significant potential for approximation of pLPs for the purpose of control. We present the resulting algorithm, based on the beneath/beyond paradigm, that computes low-complexity approximate controllers that guarantee stability and feasibility.
parametricprogramming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, re...
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parametricprogramming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, resulting in a significant decrease in on-line computation effort. In this paper we survey recent work on parametric linear programming (pLP) from the point of view of the control engineer. We identify three types of algorithms, two arising from standard convex hull paradigms and one from a geometric intuition, and classify all currently proposed methods tinder these headings. Through this classification, we identify a third standard convex hull approach that offers significant potential for approximation of pLPs for the purpose of control. We present the resulting algorithm, based on the beneath/beyond paradigm, that computes low-complexity approximate controllers that guarantee stability and feasibility.
Clustering attempts to partition a dataset into a meaningful set of mutually exclusive clusters. It is known that sequential clustering algorithms can give optimal partitions when applied to an ordered set of objects....
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Clustering attempts to partition a dataset into a meaningful set of mutually exclusive clusters. It is known that sequential clustering algorithms can give optimal partitions when applied to an ordered set of objects. In this technical note, we explore how this approach could be generalized to partition datasets in which there is no natural sequential ordering of the objects. As such, it extends the application of sequential clustering algorithms to all sets of objects. (C) 2006 Elsevier Ltd. All rights reserved.
Many problems that appear in different contexts are conceptually similar and so are amenable to solution by a common technique. Three such technical Information Systems (IS) problems are: (1) segmentation of computer ...
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Many problems that appear in different contexts are conceptually similar and so are amenable to solution by a common technique. Three such technical Information Systems (IS) problems are: (1) segmentation of computer programs;(2) attribute discretization for decision tree induction;and (3) design of hash tables in database systems. In this paper we show how each of these seemingly different problems can be formulated as a sequential (set) partitioning problem, and solved using a parametric linear programming (LP) procedure. This approach provides optimal solutions unlike previous solution approaches which were either greedy heuristics or limited to solving only a specific problem situation. Given the likelihood that other applications of the sequential partitioning problem exist in IS, the material presented here could be useful to other researchers in formulating the problem at an appropriate level of abstraction so that available optimal solution approaches can be identified. In addition to providing a common solution method, parametric LP frees the user from having to make premature decisions regarding the number of groups for the partition, and this decision can be delayed post solution. (c) 2005 Elsevier Ltd. All rights reserved.
This paper presents an improved algorithm for solving the sum of linear fractional functions (SOLF) problem in 1-D and 2-D. A key subproblem to our solution is the off-line ratio query (OLRQ) problem, which asks to fi...
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This paper presents an improved algorithm for solving the sum of linear fractional functions (SOLF) problem in 1-D and 2-D. A key subproblem to our solution is the off-line ratio query (OLRQ) problem, which asks to find the optimal values of a sequence of m linear fractional functions (called ratios), each ratio subject to a feasible domain defined by O(n) linear constraints. Based on some geometric properties and the parametric linear programming technique, we develop an algorithm that solves the OLRQ problem in O((m+n)log (m+n)) time. The OLRQ algorithm can be used to speed up every iteration of a known iterative SOLF algorithm, from O(m(m+n)) time to O((m+n)log (m+n)), in 1-D and 2-D. Implementation results of our improved 1-D and 2-D SOLF algorithm have shown that in most cases it outperforms the commonly-used approaches for the SOLF problem. We also apply our techniques to some problems in computational geometry and other areas, improving the previous results.
This paper presents a parametric approach for duality in fuzzy multi-criteria and multi-constraint level linearprogramming ((MCLP)-L-2) which extends fuzzy linearprogramming approaches. First, the MC2-simplex method...
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This paper presents a parametric approach for duality in fuzzy multi-criteria and multi-constraint level linearprogramming ((MCLP)-L-2) which extends fuzzy linearprogramming approaches. First, the MC2-simplex method is used to solve the crisp prima-dual (MCLP)-L-2 pair and then, through these crisp formulations, separate membership functions are constructed for fuzzy primal and dual program by considering the corresponding primal and dual decisions. For each program, a set of fuzzy potential solutions is determined in terms of the membership function and the related optimal solution with a certain range of decision parameters. Finally, using the primal and dual membership functions, a fuzzy weak duality function is obtained for any pair of primal and dual fuzzy potential solutions. (C) 2002 Elsevier Science B.V. All rights reserved.
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