Co-design for control and scheduling over wireless industrial control networks

Peters, Edwin G. W.; Quevedo, Daniel E.; Fu, Minyue

doi:10.1109/cdc.2015.7402577

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…Peters et al consider a spatially distributed WCN involving multiple actuators and sensors: the communication occurs over an IEEE 802.15.4–based network, where only a limited amount of transmission slots are available with respect to the number of actuators. They provided an optimal scheduling policy based on the solution of a linear‐quadratic regulator (LQR) problem for a system with intermittent measurements that is static, whereas in this paper, we provide a dynamic allocation of scheduling and routing.…”

Section: Introductionmentioning

confidence: 99%

Codesign of controller, routing and scheduling in WirelessHART networked control systems

Girolamo

D’Innocenzo

2019

Intl J Robust & Nonlinear

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Summary In this paper, we provide a novel methodology to co‐design controller, scheduling and routing in a wireless control network compliant with the WirelessHART protocol. We both provide a modeling framework and derive a novel suboptimal solution to the linear‐quadratic regulator problem for a class of systems that extends Markov jump linear system considering both continuous and discrete inputs. To allow that, our results can be directly implemented in a real WirelessHART network, we setup a receding horizon optimization problem that takes into account the constraint for compliance with WirelessHART and validate our solution on a batch reactor control loop.

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Section: Introductionmentioning

confidence: 99%

Codesign of controller, routing and scheduling in WirelessHART networked control systems

Girolamo

D’Innocenzo

2019

Intl J Robust & Nonlinear

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“…The joint Doppler scaling factor induced by the node mobility is thus ϕ m ∼ = v σ , where σ is the speed of electromagnetic waves in water and v is the relative velocity. The relative velocity of the two sensor nodes based on the joint Doppler scaling factor ϕ m can be directly obtained from [22]:…”

Section: Model Of Ar-mac Protocolmentioning

confidence: 99%

“…the φ signifies clock skew and ϕ gh is Doppler scaling factor for sensors g and h. Finally, the propagation delay is [22]:…”

Section: Model Of Ar-mac Protocolmentioning

confidence: 99%

“…Describing f (t) as the transmitted signal sent from sensor node g to h, the expression of the transmitted signal is [22]:…”

Section: Model Of Ar-mac Protocolmentioning

confidence: 99%

“…The summation of the signals that arrived along multiple physical paths is the received signal at the sink node and can be formulated as [22]:…”

Section: Model Of Ar-mac Protocolmentioning

confidence: 99%

See 2 more Smart Citations

The Alive-in-Range Medium Access Control Protocol to Optimize Queue Performance in Underwater Wireless Sensor Networks

Raina¹,

Jha²,

Bhattacharya³

2017

JTIT

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Abstract-Time synchronization between sensor nodes to reduce the end-to-end delay for critical and real time data monitoring can be achieved by cautiously monitoring the mobility of the mobile sink node in underwater wireless sensor networks. The Alive-in-Range Medium Access Control (AR-MAC) protocol monitors the delay of sensitive, critical and real-time data. The idea evolves as it involves reduction in duty cycle, precise time scheduling of active/sleep cycles of the sensors, monitoring the mobility of the sink node with the selection of appropriate queues and schedulers. The model for the path loss due to attenuation of electromagnetic wave propagation in the sea water is explained. The three-path reflection model evaluating reflection loss from the air-water and watersand interfaces as a function of distance between sensors and water depth is introduced. The algorithms for effective path determination and optimum throughput path determination are elaborated. The results verify that implementation of the Alive-in-Range MAC protocol has reduced the total number of packets dropped, the average queue length, the longest time in queue, the peak queue length and the average time in queue significantly, making it relevant for critical and real-time data monitoring.Keywords-Alive-in-Range MAC, effective path determination, mobile sink, optimum throughput path determination, underwater wireless sensor network.

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Predictive control for networked systems affected by correlated packet loss

Peters

Marelli

Quevedo

et al. 2017

Intl J Robust & Nonlinear

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In this paper, we consider the predictive control problem of designing receding horizon controllers for networked linear systems subject to random packet loss in the controller to actuator link. The packet dropouts are temporarily correlated in the sense that they obey a Markovian transition model. Our design task is to solve the optimal controller that minimizes a given receding horizon cost function, using the available packet loss history. Due to the correlated nature of the packet loss, standard linear quadratic regulator methods do not apply. We first present the optimal control law by considering the correlations. This controller turns out to depend on the packet loss history and would typically require a large lookup table for implementation when the Markovian order is high. To address this issue, we present and compare several suboptimal design approaches to reduce the number of control laws. KEYWORDSjump linear systems, networked control, optimal control, packet dropouts, predictive control, stochastic systems 5078

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Co-design for control and scheduling over wireless industrial control networks

Cited by 9 publications

References 27 publications

Codesign of controller, routing and scheduling in WirelessHART networked control systems

Codesign of controller, routing and scheduling in WirelessHART networked control systems

The Alive-in-Range Medium Access Control Protocol to Optimize Queue Performance in Underwater Wireless Sensor Networks

Predictive control for networked systems affected by correlated packet loss

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