Results 1  10
of
17
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 ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
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...
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 ..."
Abstract

Cited by 12 (8 self)
 Add to MetaCart
(Show Context)
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
Deterministic global optimization of nonlinear dynamic systems
 Eng
"... Author to whom all correspondence should be addressed. ..."
Abstract

Cited by 8 (4 self)
 Add to MetaCart
Author to whom all correspondence should be addressed.
Global Dynamic Optimization
 Massachusetts Institute of Technology
"... My thesis focuses on global optimization of nonconvex integral objective functions subject to parameter dependent ordinary differential equations. In particular, efficient, deterministic algorithms are developed for solving problems with both linear and nonlinear dynamics embedded. The techniques u ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
(Show Context)
My thesis focuses on global optimization of nonconvex integral objective functions subject to parameter dependent ordinary differential equations. In particular, efficient, deterministic algorithms are developed for solving problems with both linear and nonlinear dynamics embedded. The techniques utilized for each problem classification are unified by an underlying composition principle transferring the nonconvexity of the embedded dynamics into the integral objective function. This composition, in conjunction with control parameterization, effectively transforms the problem into a finite dimensional optimization problem where the objective function is given implicitly via the solution of a dynamic system. A standard branchandbound algorithm is employed to converge to the global solution by systematically eliminating portions of the feasible space by solving an upper bounding problem and convex lower bounding problem at each node. The novel contributions of this work lie in the derivation and solution of these convex lower bounding relaxations. Separate algorithms exist for deriving convex relaxations for problems with linear
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 ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
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...
A new approach in deterministic global optimisation of problems with ordinary differential equations
, 2003
"... ..."
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 ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
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...
Techniques for Battery Health Conscious Power Management via Electrochemical Modeling and Optimal Control
, 2011
"... Far better is it to dare mighty things, to win glorious triumphs, even though checkered by failure, than to rank with those poor spirits who neither enjoy nor suffer much, because they live in a gray twilight that knows not victory nor defeat. — Theodore Roosevelt If we knew what we were doing, it w ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
(Show Context)
Far better is it to dare mighty things, to win glorious triumphs, even though checkered by failure, than to rank with those poor spirits who neither enjoy nor suffer much, because they live in a gray twilight that knows not victory nor defeat. — Theodore Roosevelt If we knew what we were doing, it wouldn’t be called
Suboptimal Cold Start Strategies for Spark Ignition Engines
, 2010
"... Abstract — The dramatic increase of vehicle use in urban situations, coupled with the short average length of the subsequent journey, leads to a large proportion of engine operation happening under cold start conditions. The cold start results in larger fuel consumption and higher emissions relativ ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract — The dramatic increase of vehicle use in urban situations, coupled with the short average length of the subsequent journey, leads to a large proportion of engine operation happening under cold start conditions. The cold start results in larger fuel consumption and higher emissions relative to a fully warmed engine, and ultimately the minimization of both of these has led to intense and complex calibration processes. Typically, the engine calibration is a quasistatic process requiring multidimensional sweeps across engine control variables to find the best combination of inputs for each steadystate loadspeed operating condition. The use of optimal control techniques on appropriate engine models may reduce the search effort associated with calibration. This methodology is demonstrated here using a metric weighting thermal behavior and fuel use on loworder engine models with thermal dynamics to develop insights into the nature of the optimal control policies over the warmup duration. These findings are validated using a numeric optimization performed on highorder engine simulation. Future optimization approaches, however, may be simplified by utilizing the analytic results. Subsequently, the trajectories for two different objective functions are compared with a production calibration to demonstrate the proposed methodology. Index Terms — Automotive control, engine calibration, engine cold start, engine warm up, fuel economy, optimal control.