Distributed Stochastic MPC for Networked Linear Systems with a Multirate Sampling Mechanism

Hongjiu Yang; Hai Zhao; Yuanqing Xia; Yang Xu

doi:10.1109/TSMC.2020.3048998

Distributed Stochastic MPC for Networked Linear Systems with a Multirate Sampling Mechanism

Hongjiu Yang^*, Hai Zhao, Yuanqing Xia, Yang Xu

^*Corresponding author for this work

School of Automation

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

In this article, a distributed stochastic model predictive control (MPC) algorithm with a multirate sampling mechanism is proposed for a networked linear system with multiple dynamic subsystems. A delta operator approach is used for the multiple dynamic subsystems with different sampling periods in the multirate sampling mechanism. Both stochastic disturbances and probabilistic constraints of the multiple dynamic subsystems are satisfied by the distributed stochastic MPC algorithm. Packets dropout are considered by the stochastic MPC algorithm and state predicted errors are compensated. Recursive feasibility and quadratic stability are obtained for the networked linear system under the distributed stochastic MPC algorithm. A numerical example is given to illustrate effectiveness of the proposed algorithm.

Original language	English
Pages (from-to)	2250-2261
Number of pages	12
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	52
Issue number	4
DOIs	https://doi.org/10.1109/TSMC.2020.3048998
Publication status	Published - 1 Apr 2022

Keywords

Distributed model predictive control (DMPC)
multirate sampling
packets dropout
probabilistic constraints

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10.1109/TSMC.2020.3048998

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title = "Distributed Stochastic MPC for Networked Linear Systems with a Multirate Sampling Mechanism",

abstract = "In this article, a distributed stochastic model predictive control (MPC) algorithm with a multirate sampling mechanism is proposed for a networked linear system with multiple dynamic subsystems. A delta operator approach is used for the multiple dynamic subsystems with different sampling periods in the multirate sampling mechanism. Both stochastic disturbances and probabilistic constraints of the multiple dynamic subsystems are satisfied by the distributed stochastic MPC algorithm. Packets dropout are considered by the stochastic MPC algorithm and state predicted errors are compensated. Recursive feasibility and quadratic stability are obtained for the networked linear system under the distributed stochastic MPC algorithm. A numerical example is given to illustrate effectiveness of the proposed algorithm.",

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author = "Hongjiu Yang and Hai Zhao and Yuanqing Xia and Yang Xu",

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T1 - Distributed Stochastic MPC for Networked Linear Systems with a Multirate Sampling Mechanism

AU - Yang, Hongjiu

AU - Zhao, Hai

AU - Xia, Yuanqing

AU - Xu, Yang

PY - 2022/4/1

Y1 - 2022/4/1

N2 - In this article, a distributed stochastic model predictive control (MPC) algorithm with a multirate sampling mechanism is proposed for a networked linear system with multiple dynamic subsystems. A delta operator approach is used for the multiple dynamic subsystems with different sampling periods in the multirate sampling mechanism. Both stochastic disturbances and probabilistic constraints of the multiple dynamic subsystems are satisfied by the distributed stochastic MPC algorithm. Packets dropout are considered by the stochastic MPC algorithm and state predicted errors are compensated. Recursive feasibility and quadratic stability are obtained for the networked linear system under the distributed stochastic MPC algorithm. A numerical example is given to illustrate effectiveness of the proposed algorithm.

AB - In this article, a distributed stochastic model predictive control (MPC) algorithm with a multirate sampling mechanism is proposed for a networked linear system with multiple dynamic subsystems. A delta operator approach is used for the multiple dynamic subsystems with different sampling periods in the multirate sampling mechanism. Both stochastic disturbances and probabilistic constraints of the multiple dynamic subsystems are satisfied by the distributed stochastic MPC algorithm. Packets dropout are considered by the stochastic MPC algorithm and state predicted errors are compensated. Recursive feasibility and quadratic stability are obtained for the networked linear system under the distributed stochastic MPC algorithm. A numerical example is given to illustrate effectiveness of the proposed algorithm.

KW - Distributed model predictive control (DMPC)

KW - multirate sampling

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KW - probabilistic constraints

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JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

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Distributed Stochastic MPC for Networked Linear Systems with a Multirate Sampling Mechanism

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