Optimizing the end-point state-weighting matrix in model-based predictive control
type: Item
instance of: scholarly article
title: Optimizing the end-point state-weighting matrix in model-based predictive control
zbMATH Open document ID: 1010.93041
DOI: 10.1016/S0005-1098(01)00296-5
author: Hayco H. J. Bloemen
author: Ton van den Boom
author: Henk B. Verbruggen
published in: Automatica
publication date: 2002-09-05T00:00:00Z
review text: A linear model-based predictive control (MPC) algorithm is presented for which nominal closed-loop stability is guaranteed. The input is obtained by minimizing a quadratic performance index over a finite horizon plus an end-point state (EPS) penalty, subject to input, state and output constraints. Under certain conditions, the weighting matrix in the EPS penalty enables one to specify an invariant ellipsoid in which the input, state and output constraints are satisfied. In existing MPC algorithms this weighting matrix is calculated off-line. The main contribution of this paper is to incorporate the calculation of the EPS-weighting matrix into the on-line optimization problem of the controller. The main advantage of this approach is that a natural and automatic trade-off between feasibility and optimality is obtained. This is demonstrated in a simulation example.
reviewed by: Yasuo Sugai
Mathematics Subject Classification ID: 93B51
Mathematics Subject Classification ID: 93C55
zbMATH DE Number: 1797085
zbMATH Keywords: predictive control
zbMATH Keywords: stability
zbMATH Keywords: constraints
zbMATH Keywords: end-point state penalty
zbMATH Keywords: invariant ellipsoid
zbMATH Keywords: on-line optimization
MaRDI profile type: Publication
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full work available at URL: s0005-1098(01)00296-5
OpenAlex ID: W1993950511
description: scientific article; zbMATH DE number 1797085