Generalized predictive control in the delta-domain

Morten Bach Lauritsen; Morten Rostgaard Jensen; Niels Kjølstad Poulsen

Generalized predictive control in the delta-domain

Morten Bach Lauritsen, Morten Rostgaard Jensen, Niels Kjølstad Poulsen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers are considered: one having a well-defined limit as the sampling period tends to zero, the other being a close approximation to the conventional discrete-time GPC. Both algorithms are discrete in nature and well-suited for adaptive control. The fact, that δ-domain model are used does not introduce an approximation since such a model can be obtained by an exact sampling of a continuous-time model.

Original language	English
Title of host publication	Proceedings of the American Control Conference
Volume	Volume 5
Publisher	IEEE
Publication date	1995
Pages	3709-3713
ISBN (Print)	07-80-32445-5
Publication status	Published - 1995
Event	1995 American Control Conference - Seattle, WA, United States Duration: 21 Jun 1995 → 23 Jun 1995 http://www-control.eng.cam.ac.uk/extras/conferences/ACC95.html

Conference

Conference	1995 American Control Conference
Country	United States
City	Seattle, WA
Period	21/06/1995 → 23/06/1995
Internet address	http://www-control.eng.cam.ac.uk/extras/conferences/ACC95.html

Bibliographical note

Copyright 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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title = "Generalized predictive control in the delta-domain",

abstract = "This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers are considered: one having a well-defined limit as the sampling period tends to zero, the other being a close approximation to the conventional discrete-time GPC. Both algorithms are discrete in nature and well-suited for adaptive control. The fact, that δ-domain model are used does not introduce an approximation since such a model can be obtained by an exact sampling of a continuous-time model.",

author = "Lauritsen, {Morten Bach} and Jensen, {Morten Rostgaard} and Poulsen, {Niels Kj{\o}lstad}",

note = "Copyright 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; 1995 American Control Conference ; Conference date: 21-06-1995 Through 23-06-1995",

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AU - Jensen, Morten Rostgaard

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N1 - Copyright 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 1995

Y1 - 1995

N2 - This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers are considered: one having a well-defined limit as the sampling period tends to zero, the other being a close approximation to the conventional discrete-time GPC. Both algorithms are discrete in nature and well-suited for adaptive control. The fact, that δ-domain model are used does not introduce an approximation since such a model can be obtained by an exact sampling of a continuous-time model.

AB - This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers are considered: one having a well-defined limit as the sampling period tends to zero, the other being a close approximation to the conventional discrete-time GPC. Both algorithms are discrete in nature and well-suited for adaptive control. The fact, that δ-domain model are used does not introduce an approximation since such a model can be obtained by an exact sampling of a continuous-time model.

M3 - Article in proceedings

SN - 07-80-32445-5

VL - Volume 5

SP - 3709

EP - 3713

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