Application Set Approximation in Optimal Input Design for Model Predictive Control
2014 (English)In: 2014 European Control Conference (ECC), 2014, 744-749 p.Conference paper (Refereed)
This contribution considers one central aspect of experiment design in system identification, namely application set approximation. When a control design is based on an estimated model, the achievable performance is related to the quality of the estimate. The degradation in control performance due to plant-modeling missmatch is quantified by an application cost function. A convex approximation of the set of models that satisfy the control specification is typically required in optimal input design. The standard approach is to use a quadratic approximation of the application cost function, where the main computational effort is to find the corresponding Hessian matrix. Our main contribution is an alternative approach for this problem, which uses the structure of the underlying optimal control problem to considerably reduce the computations needed to find the application set. This technique allows the use of applications oriented input design for MPC on much more complex plants. The approach is numerically evaluated on a distillation control problem.
Place, publisher, year, edition, pages
2014. 744-749 p.
Cost functions, Design, Distillation, Model predictive control, Optimal control systems, Predictive control systems, Achievable performance, Computational effort, Control specifications, Convex approximation, Distillation control, Optimal control problem, Optimal input design, Quadratic approximation
IdentifiersURN: urn:nbn:se:kth:diva-137203DOI: 10.1109/ECC.2014.6862496ISI: 000349955701007ScopusID: 2-s2.0-84911468844ISBN: 978-3-9524269-1-3OAI: oai:DiVA.org:kth-137203DiVA: diva2:678272
13th European Control Conference, ECC 2014, Strasbourg Convention and Exhibition Center Place de Bordeaux Strasbourg, France, 24 June 2014 through 27 June 2014
QC 201401132013-12-112013-12-112015-04-09Bibliographically approved