Event-driven control with deadline optimization for linear systems with stochastic delays

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
12 Downloads (Pure)

Abstract

This work presents a novel control strategy for systems with actuation delays with known stochastic distribution, which improves upon previously proposed deadline-driven and event-driven strategies. In the event-driven strategy, the control input is immediately updated after the delay, whereas in the deadline-driven strategy, the actuation is updated in a periodic fashion, where the sampling period sets a deadline; if the delay is larger than this deadline, the actuation is not updated. Our method switches between these two strategies and guarantees better performance, in a linear-quadratic-Gaussian sense, than either method considered separately. An extension of the novel method with a deadline-optimization scheme is shown to improve the performance even further. Simulation results illustrate the effectiveness of the proposed methods.

Original languageEnglish
Article number8069000
Pages (from-to)1819-1829
Number of pages11
JournalIEEE Transactions on Control of Network Systems
Volume5
Issue number4
DOIs
Publication statusPublished - 1 Dec 2018

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Event-driven
Deadline
Linear systems
Linear Systems
Optimization
Switches
Sampling
Performance Guarantee
Immediately
Control Strategy
Switch
Strategy
Simulation

Keywords

  • Data loss
  • dynamic programming
  • event-driven control
  • sampled-data control
  • self-triggered control
  • stochastic optimal control
  • stochastic time delay

Cite this

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title = "Event-driven control with deadline optimization for linear systems with stochastic delays",
abstract = "This work presents a novel control strategy for systems with actuation delays with known stochastic distribution, which improves upon previously proposed deadline-driven and event-driven strategies. In the event-driven strategy, the control input is immediately updated after the delay, whereas in the deadline-driven strategy, the actuation is updated in a periodic fashion, where the sampling period sets a deadline; if the delay is larger than this deadline, the actuation is not updated. Our method switches between these two strategies and guarantees better performance, in a linear-quadratic-Gaussian sense, than either method considered separately. An extension of the novel method with a deadline-optimization scheme is shown to improve the performance even further. Simulation results illustrate the effectiveness of the proposed methods.",
keywords = "Data loss, dynamic programming, event-driven control, sampled-data control, self-triggered control, stochastic optimal control, stochastic time delay",
author = "{van Horssen}, E.P. and S. Prakash and {Guerreiro Tom{\'e} Antunes}, D.J. and W.P.M.H. Heemels",
year = "2018",
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Event-driven control with deadline optimization for linear systems with stochastic delays. / van Horssen, E.P.; Prakash, S.; Guerreiro Tomé Antunes, D.J.; Heemels, W.P.M.H.

In: IEEE Transactions on Control of Network Systems, Vol. 5, No. 4, 8069000, 01.12.2018, p. 1819-1829.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Event-driven control with deadline optimization for linear systems with stochastic delays

AU - van Horssen, E.P.

AU - Prakash, S.

AU - Guerreiro Tomé Antunes, D.J.

AU - Heemels, W.P.M.H.

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AB - This work presents a novel control strategy for systems with actuation delays with known stochastic distribution, which improves upon previously proposed deadline-driven and event-driven strategies. In the event-driven strategy, the control input is immediately updated after the delay, whereas in the deadline-driven strategy, the actuation is updated in a periodic fashion, where the sampling period sets a deadline; if the delay is larger than this deadline, the actuation is not updated. Our method switches between these two strategies and guarantees better performance, in a linear-quadratic-Gaussian sense, than either method considered separately. An extension of the novel method with a deadline-optimization scheme is shown to improve the performance even further. Simulation results illustrate the effectiveness of the proposed methods.

KW - Data loss

KW - dynamic programming

KW - event-driven control

KW - sampled-data control

KW - self-triggered control

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