Application Set Approximation in Optimal Input Design for Model Predictive Control

This contribution considers one central aspect of experiment design in system identification. 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 errors in the estimated model is mea...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2013-03
Main Authors: Afrooz Ebadat, Annergren, Mariette, Larsson, Christian A, Rojas, Cristian R, Wahlberg, Bo
Format: Article
Language:English
Subjects:
Approximation
Cost function
Design of experiments
Design optimization
Distillation
Hessian matrices
Mathematical analysis
Mathematical models
Optimal control
Predictive control
System identification
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This contribution considers one central aspect of experiment design in system identification. 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 errors in the estimated model is measured by an application cost function. In order to use an optimization based input design method, a convex approximation of the set of models that atisfies the control specification is required. 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.
ISSN:2331-8422