A numerically robust state-space approach to stable-predictive control strategies
type: Item
instance of: scholarly article
title: A numerically robust state-space approach to stable-predictive control strategies
zbMATH Open document ID: 0913.93022
DOI: 10.1016/S0005-1098(97)00171-4
author: J. Rossiter
author: M. J. Rice
author: Basil Kouvaritakis
published in: Automatica
publication date: 1998-08-20T00:00:00Z
review text: The stable general predictive control (SGPC) transfer function framework is extended to a state-space representation which has significant computational and numerical advantages in the case of an open-loop unstable plant. The key idea is prestabilization of the plant before the predictions (for the SGPC algorithms) are computed. Prestabilization ensures stable and bounded predictions which improves numerical conditioning hence the use of matrices with very large elements associated with unstable predictions is avoided. Prestabilization is later removed through optimization. It is shown that the proposed alternative to a SGPC algorithmic formulation for state-space control laws does not suffer from poor conditioning and meets most terminal constraints imposed by the SGPC approach.
Mathematics Subject Classification ID: 93B51
Mathematics Subject Classification ID: 93B40
zbMATH DE Number: 1192907
zbMATH Keywords: stable general predictive control
zbMATH Keywords: state-space representation
zbMATH Keywords: prestabilization
zbMATH Keywords: numerical conditioning
reviewed by: Ingmar Randvee
MaRDI profile type: Publication
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Wikidata QID: Q127097541
description: scientific article; zbMATH DE number 1192907