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Optimal On-line (m,k)-firm Constraint Assignment for Real-time Control Tasks Based on Plant State Information

Identifieur interne : 003955 ( Hal/Curation ); précédent : 003954; suivant : 003956

Optimal On-line (m,k)-firm Constraint Assignment for Real-time Control Tasks Based on Plant State Information

Auteurs : Flavia Felicioni [France] ; Ning Jia [France] ; Françoise Simonot-Lion [France] ; Ye-Qiong Song [France]

Source :

RBID : Hal:inria-00335942

Abstract

In this paper, we study the problem of scheduling a set of control tasks. We distinguish three different situations of states of controlled plants: not activated, steady state situation and transient situation. The infinite-horizon and finite-horizon cost functions are respectively used to represent the performance of each control task in last two situations. We propose a scheduling architecture in which, according to the plant state situation, the task handler switches between these two types of performance criterion to determine an on-line (m,k)-constraint based control task scheduling strategy, so that the overall control performance is maintained at a high level in each situation subject to the task schedulability. The approach is exemplified on a set of controllers for different plants.

Url:
DOI: 10.1109/ETFA.2008.4638504

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Hal:inria-00335942

Le document en format XML

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<div type="abstract" xml:lang="en">In this paper, we study the problem of scheduling a set of control tasks. We distinguish three different situations of states of controlled plants: not activated, steady state situation and transient situation. The infinite-horizon and finite-horizon cost functions are respectively used to represent the performance of each control task in last two situations. We propose a scheduling architecture in which, according to the plant state situation, the task handler switches between these two types of performance criterion to determine an on-line (m,k)-constraint based control task scheduling strategy, so that the overall control performance is maintained at a high level in each situation subject to the task schedulability. The approach is exemplified on a set of controllers for different plants.</div>
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