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Techniques in Computational Stochastic Dynamic Programming
 in Control and Dynamic Systems
, 1996
"... INTRODUCTION When Bellman introduced dynamic programming in his original monograph [8], computers were not as powerful as current personal computers. Hence, his description of the extreme computational demands as the Curse of Dimensionality [9] would not have had the super and massively parallel p ..."
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Cited by 12 (8 self)
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INTRODUCTION When Bellman introduced dynamic programming in his original monograph [8], computers were not as powerful as current personal computers. Hence, his description of the extreme computational demands as the Curse of Dimensionality [9] would not have had the super and massively parallel processors of today in mind. However, massive and super computers can not overcome the Curse of Dimensionality alone, but parallel and vector computation can permit the solution of higher dimension than was previously possible and thus permit more realistic dynamic programming applications. Today such large problems are called Grand and National Challenge problems [45, 46] in high performance computing. Today's availability of high performance vector supercomputers and massively parallel processors have made it possible to compute optimal policies and values of control systems for much larger dimensions than was possible earlier. Advance
An Evolutionary Strategy for FedBatch Bioreactor Optimization; Concepts and Performance
, 1999
"... An evolutionary program, based on a realcode genetic algorithm (GA), is applied to calculate optimal control policies for bioreactors. The GA is used as a nonlinear optimizer in combination with simulation software and constraint handling procedures. A new class of GAoperators is introduced to obt ..."
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Cited by 11 (0 self)
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An evolutionary program, based on a realcode genetic algorithm (GA), is applied to calculate optimal control policies for bioreactors. The GA is used as a nonlinear optimizer in combination with simulation software and constraint handling procedures. A new class of GAoperators is introduced to obtain smooth control trajectories, which leads also to a drastic reduction in computational load. The proposed method is easy to understand and has no restrictions on the model type and structure. The performance and optimal trajectories obtained by the extended GA are compared with those calculated with two common methods: (i) dynamic programming, and (ii) a Hamiltonian based gradient algorithm. The GA proved to be a good and often superior alternative for solving optimal control problems. 1999 Elsevier Science B.V. All rights reserved. Keywords: Fedbatch bioreactor; Optimal control; Genetic algorithm; Evolutionary program 1. Introduction Bioreactors are often operated in batch or fedbatc...
Deterministic global optimization of nonlinear dynamic systems
 Eng
"... Author to whom all correspondence should be addressed. ..."
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Cited by 6 (4 self)
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Author to whom all correspondence should be addressed.
Control Techniques For PhysicallyBased Animation

, 1994
"... Determining how an animal should activate its muscles to perform a desired motion is a difficult problem. Solutions to this problem are useful to animators who wish to direct their characters without having to provide all the explicit details of a motion as required in keyframing. This thesis unites ..."
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Cited by 4 (0 self)
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Determining how an animal should activate its muscles to perform a desired motion is a difficult problem. Solutions to this problem are useful to animators who wish to direct their characters without having to provide all the explicit details of a motion as required in keyframing. This thesis unites some of the ideas from the field of control with the requirements of animators. Four contributions towards solving motion control problems are presented. The first contribution, that of approximatinggraph dynamic programming, is an enhancement of a general optimal control technique. It proves useful for problems of low dimension, such as balancing a pole or parking a truckandtrailer. Our second contribution is a variable terrain walking controller for a human figure. This shows how decomposition of a complex control problem can be used to make it tractable. Our third result consists of two methods of control for the turning motions typically performed by bicyclists and skiers. The contro...
Fuzzy modelbased predictive control using TakagiSugeno models
, 1999
"... Nonlinear modelbased predictive control (MBPC) in multiinput multioutput (MIMO) process control is attractive for industry. However, two main problems need to be considered: (i) obtaining a good nonlinear model of the process, and (ii) applying the model for control purposes. In this paper, recen ..."
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Cited by 3 (0 self)
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Nonlinear modelbased predictive control (MBPC) in multiinput multioutput (MIMO) process control is attractive for industry. However, two main problems need to be considered: (i) obtaining a good nonlinear model of the process, and (ii) applying the model for control purposes. In this paper, recent work focusing on the use of TakagiSugeno fuzzy models in combination with MBPC is described. First, the fuzzy modelidentification of MIMO processes is given. The process model is derived from inputoutput data by means of productspace fuzzy clustering. The MIMO model is represented as a set of coupled multiinput, singleoutput (MISO) models. Next, the TakagiSugeno fuzzy model is used in combination with MBPC. The critical element in nonlinear MBPC is the optimization routine which is nonconvex and thus difficult to solve. Two methods to deal with this problem are developed: (i) a branchandbound method with iterative gridsize reduction, and (ii) control based on a local linear model. Both m...
Optimization Framework for the Synthesis of Chemical Reactor Networks
, 1998
"... The reactor network synthesis problem involves determining the type, size, and interconnections of the reactor units, optimal concentration and temperature profiles, and the heat load requirements of the process. A general framework is presented for the synthesis of optimal chemical reactor networks ..."
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Cited by 2 (1 self)
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The reactor network synthesis problem involves determining the type, size, and interconnections of the reactor units, optimal concentration and temperature profiles, and the heat load requirements of the process. A general framework is presented for the synthesis of optimal chemical reactor networks via an optimization approach. The possible design alternatives are represented via a process superstructure which includes continuous stirred tank reactors and cross flow reactors along with mixers and splitters that connect the units. The superstructure is mathematically modeled using differential and algebraic constraints and the resulting problem is formulated as an optimal control problem. The solution methodology for addressing the optimal control formulation involves the application of a control parameterization approach where the selected control variables are discretized in terms of time invariant parameters. The dynamic system is decoupled from the optimization and solved as a func...
Departamento de Informatica y Automatica
"... dynamic models Abstract: The paper deals with chemical processes which require dynamical optimization; such is the case of batch fermentation processes. For study, beer fermentation was selected as a good paradigm: the process is controlled by a temperature profile along a period of time. The object ..."
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dynamic models Abstract: The paper deals with chemical processes which require dynamical optimization; such is the case of batch fermentation processes. For study, beer fermentation was selected as a good paradigm: the process is controlled by a temperature profile along a period of time. The objective is to accelerate the process, finding a good profile, under some constraints. It was decided to keep industrial conditions, not reflected in the literature, so it was needed an extensive laboratory work to find a new model. Having obtained the model, optimization studies started with dynamic programming, and found serious difficulties, so the use of genetic algorithms was explored, by a special encoding of the problem, attaining successful results. The paper describes the problem, the model, how to apply a genetic algorithm, and details of the results. 1.
Global Optimization with NonAnalytical Constraints
"... This paper presents an approach for the global optimization of constrained nonlinear programming problems in which some of the constraints are nonanalytical (nonfactorable), defined by a computational model for which no explicit analytical representation is available. ..."
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This paper presents an approach for the global optimization of constrained nonlinear programming problems in which some of the constraints are nonanalytical (nonfactorable), defined by a computational model for which no explicit analytical representation is available.