Adaptive predictive control of the benchmark plant

doi:10.1016/0005-1098(94)90150-3

Automatica

Volume 30, Issue 4, April 1994, Pages 621-628

https://doi.org/10.1016/0005-1098(94)90150-3 Get rights and content

Abstract

One of the main motives for adaptation is to design a high-performance controller for time-varying systems. The benchmark plant is a time-varying system whose behaviour is not known. This paper applies an adaptive predictive control scheme, based on CRHPC, to the benchmark plant using minimal prior information, and shows that the adaptive controller with appropriately chosen filters can cope well with time-variations.

References (10)

S.F. Graebe
Robust and adaptive control of an unknown plant: a benchmark of new format
Automatica
(1994)
E. Mosca et al.
Stable redesign of predictive control
Automatica
(1992)
K.J. Åström et al.
D.W. Clarke et al.
Constrained receding-horizon predictive control
R.H. Middleton et al.
Adaptive control of time-varying linear systems
IEEE Trans. on Auto. Control
(1988)

There are more references available in the full text version of this article.

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^☆: The original version of this paper was presented at the 12th IFAC World Congress which was held in Sydney, Australia, during 19–23 July 1993. The Published Proceedings of this IFAC Meeting may be ordered from Elsevier Science Limited, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, U.K. This paper was recommended for publication in revised form by Guest Editor S. Graebe under the direction of Editor H. Kwakernaak.

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Special section paperAdaptive predictive control of the benchmark plant☆

Abstract

Automatica

Automatica

Constrained receding-horizon predictive control

Adaptive control of time-varying linear systems

IEEE Trans. on Auto. Control

Special section paper
Adaptive predictive control of the benchmark plant☆