This paper proposes an analytical framework for co-designing wireless networked control systems (WNCSs) demonstrated in an industrial scenario. The framework allows us to quantitatively characterize the impact of wireless channel model accuracy when designing a controller to stabilize a WNCS. We consider a scenario consisting of two co-located wireless networks: the first connects the plant automation network backbone to field devices via WirelessHART, ISA-100.11a, or IEEE 802.15.4e, and the second uses IEEE 802.11 equipment to supply real-time multimedia data to the supervisory devices. First, we derive a parametric 802.11 interference characterization for an arbitrary number of active interfering devices and perform extensive parametric analysis. We then derive the message and packet error probability expressions necessary to develop an appropriate finite-state Markov channel model. Finally, we ran sizeable Monte Carlo simulations to evaluate the impact of our channel model on the control performance of a wireless closed-loop system and compared it with the performance obtained using a Bernoulli channel model.

Co-Designing Wireless Networked Control Systems on IEEE 802.15.4-Based Links Under Wi-Fi Interference

Lun, Yuriy Zacchia
;
Rinaldi, Claudia;D'Innocenzo, Alessandro;Santucci, Fortunato
2024-01-01

Abstract

This paper proposes an analytical framework for co-designing wireless networked control systems (WNCSs) demonstrated in an industrial scenario. The framework allows us to quantitatively characterize the impact of wireless channel model accuracy when designing a controller to stabilize a WNCS. We consider a scenario consisting of two co-located wireless networks: the first connects the plant automation network backbone to field devices via WirelessHART, ISA-100.11a, or IEEE 802.15.4e, and the second uses IEEE 802.11 equipment to supply real-time multimedia data to the supervisory devices. First, we derive a parametric 802.11 interference characterization for an arbitrary number of active interfering devices and perform extensive parametric analysis. We then derive the message and packet error probability expressions necessary to develop an appropriate finite-state Markov channel model. Finally, we ran sizeable Monte Carlo simulations to evaluate the impact of our channel model on the control performance of a wireless closed-loop system and compared it with the performance obtained using a Bernoulli channel model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/232180
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