We consider the problem of optimal allocation of computation resources to a set of control tasks sharing a CPU. Each task is used to control a linear and time invariant plant affected by noise and is supposed to have a computation time with known distribution. The metric we use to express the Quality of Control (QoC) is a function of the steady state covariance of the state. We show how the combination of a Resource Reservation scheduler with an unconventional model of computation known as Continuous Stream produces a system that is easy to analyse. In particular, it is possible to compute the QoC of each control loop as a function of the fraction of CPU (bandwidth) that the task receives, and formalise an optimisation problem where a global QoC metric is defined that consolidates the QoC of each task. We show an efficient solution to this optimisation problem and validate its efficacy on a set of numeric examples.
Optimal mean square control using the continuous stream model of computation / Fontanelli, Daniele; Greco, Luca; Palopoli, Luigi. - ELETTRONICO. - 2016-:(2015), pp. 1958-1965. ( 54th IEEE Conference on Decision and Control, CDC 2015 Osaka International Convention Center (Grand Cube), 5-3-51 Nakanoshima, Kita-Ku, jpn 2015) [10.1109/CDC.2015.7402494].
Optimal mean square control using the continuous stream model of computation
Fontanelli, Daniele;Palopoli, Luigi
2015-01-01
Abstract
We consider the problem of optimal allocation of computation resources to a set of control tasks sharing a CPU. Each task is used to control a linear and time invariant plant affected by noise and is supposed to have a computation time with known distribution. The metric we use to express the Quality of Control (QoC) is a function of the steady state covariance of the state. We show how the combination of a Resource Reservation scheduler with an unconventional model of computation known as Continuous Stream produces a system that is easy to analyse. In particular, it is possible to compute the QoC of each control loop as a function of the fraction of CPU (bandwidth) that the task receives, and formalise an optimisation problem where a global QoC metric is defined that consolidates the QoC of each task. We show an efficient solution to this optimisation problem and validate its efficacy on a set of numeric examples.| File | Dimensione | Formato | |
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OptimalCovarianceCS.pdf
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