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SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 597 (24 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available, and that the constraint gradients are sparse. We discuss
TrustRegion InteriorPoint SQP Algorithms For A Class Of Nonlinear Programming Problems
 SIAM J. CONTROL OPTIM
, 1997
"... In this paper a family of trustregion interiorpoint SQP algorithms for the solution of a class of minimization problems with nonlinear equality constraints and simple bounds on some of the variables is described and analyzed. Such nonlinear programs arise e.g. from the discretization of optimal co ..."
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Cited by 46 (9 self)
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In this paper a family of trustregion interiorpoint SQP algorithms for the solution of a class of minimization problems with nonlinear equality constraints and simple bounds on some of the variables is described and analyzed. Such nonlinear programs arise e.g. from the discretization of optimal control problems. The algorithms treat states and controls as independent variables. They are designed to take advantage of the structure of the problem. In particular they do not rely on matrix factorizations of the linearized constraints, but use solutions of the linearized state equation and the adjoint equation. They are well suited for large scale problems arising from optimal control problems governed by partial differential equations. The algorithms keep strict feasibility with respect to the bound constraints by using an affine scaling method proposed for a different class of problems by Coleman and Li and they exploit trustregion techniques for equalityconstrained optimizatio...
Formulation of dynamic optimization problems using Modelica and their efficient solution
, 2002
"... Dynamic optimization problems often arise in advanced model based control. For example in model based predictive control and in the estimation of process parameters or not measured process signals, the underlying problems can be treated with optimization. A process model formulated in Modelica [10] ..."
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Cited by 7 (1 self)
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Dynamic optimization problems often arise in advanced model based control. For example in model based predictive control and in the estimation of process parameters or not measured process signals, the underlying problems can be treated with optimization. A process model formulated in Modelica [10] can be used as a core pan in the formulation of dynamic optimization problems. This allows an efficient engineering of advanced control applications as simulation models are reused for optimization.
Infinitedimensional Optimization and Optimal Design
, 2003
"... Formulation In the most general form, we can write an optimization problem in a topological space endowed with some topology and J : R is the objective functional. By extending the objective functional to U via J(u) := we can rewrite this problem as . ..."
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Formulation In the most general form, we can write an optimization problem in a topological space endowed with some topology and J : R is the objective functional. By extending the objective functional to U via J(u) := we can rewrite this problem as .
Online Application of Modelica Models in the Industrial IT Extended Automation System 800xA
"... The Modelica technology and the increasing availability of model libraries allow an efficient modeling of complex dynamic processes. Having a good process model at hand one might want to apply the model online to improve the operation of the real process. These online applications range from the g ..."
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The Modelica technology and the increasing availability of model libraries allow an efficient modeling of complex dynamic processes. Having a good process model at hand one might want to apply the model online to improve the operation of the real process. These online applications range from the generation of highlevel information like performance indices from process measurements over the estimation of unmeasured quantities in a so called soft sensor up to model based control and online optimization. This paper discusses the online application of Modelica models in an industrial control system. The models are developed and tested using a standard Modelica tool. Afterwards they are imported into the control system. Here the model variables can be associated with process signals. This way a model can be initialized with current process values. A numerical solver performs simulation, estimation or optimization activities. Solution results can either be used for diagnostics or they can be fed back to the process as manipulated variables. The Dynamic Optimization system extension has been developed for the Industrial IT System 800xA. Exploiting Aspect Object technology, the required functionality for modelbased applications can be integrated seamlessly with the control system. Model based applications can be set up in a modularly structured way. The Dynamic Optimization system extension has been used to deploy different modelbased applications. A Nonlinear Modelbased Predictive Controller (NMPC) for the startup of steam power plants is discussed as an example. The overall NMPC application consists of several modelbased activities, including preprocessing of process values, estimation of model states, prediction of optimal operations, and postprocessing of optimization results. A scheduler periodically triggers these activities online.