Distribution consensus of multi-Agent systems based on model predictive control with probability density function

Lian Geng; Maopeng Ran; Kexin Liu

doi:10.1109/CFASTA57821.2023.10243316

Distribution consensus of multi-Agent systems based on model predictive control with probability density function

Lian Geng
, Maopeng Ran
, Kexin Liu

School of Automation Science and Electrical Engineering

Beihang University
Zhongguancun Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A distributed output feedback model predictive control approach based on the probability density function method is proposed to realize multi-Agent distribution consensus. Each agent solves the optimal control input by estimating the worst-case local error and perturbation, modeled into a local min-max optimization problem. In the iterative solving process, the agent i will send its information to its neighbor agent through the communication topology, so as to achieve the convergence of group consensus error. Under the assumption of controllability and observability, the proposed control method provides an upper bound for the group distribution consensus error, thus ensuring the practical distribution consensus performance under unmeasured interference and noise.

Original language	English
Title of host publication	Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	343-348
Number of pages	6
ISBN (Electronic)	9798350332162
DOIs	https://doi.org/10.1109/CFASTA57821.2023.10243316
State	Published - 2023
Event	2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 - Qingdao, China Duration: 14 Jul 2023 → 16 Jul 2023

Publication series

Name	Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

Conference

Conference	2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023
Country/Territory	China
City	Qingdao
Period	14/07/23 → 16/07/23

Keywords

Distribution Consensus
Model Predictive Control
Multi-Agent
Probability Density Function

Access to Document

10.1109/CFASTA57821.2023.10243316

Cite this

Geng, L., Ran, M., & Liu, K. (2023). Distribution consensus of multi-Agent systems based on model predictive control with probability density function. In Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 (pp. 343-348). (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CFASTA57821.2023.10243316

Geng, Lian ; Ran, Maopeng ; Liu, Kexin. / Distribution consensus of multi-Agent systems based on model predictive control with probability density function. Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 343-348 (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023).

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title = "Distribution consensus of multi-Agent systems based on model predictive control with probability density function",

abstract = "A distributed output feedback model predictive control approach based on the probability density function method is proposed to realize multi-Agent distribution consensus. Each agent solves the optimal control input by estimating the worst-case local error and perturbation, modeled into a local min-max optimization problem. In the iterative solving process, the agent i will send its information to its neighbor agent through the communication topology, so as to achieve the convergence of group consensus error. Under the assumption of controllability and observability, the proposed control method provides an upper bound for the group distribution consensus error, thus ensuring the practical distribution consensus performance under unmeasured interference and noise.",

keywords = "Distribution Consensus, Model Predictive Control, Multi-Agent, Probability Density Function",

author = "Lian Geng and Maopeng Ran and Kexin Liu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023 ; Conference date: 14-07-2023 Through 16-07-2023",

year = "2023",

doi = "10.1109/CFASTA57821.2023.10243316",

language = "英语",

series = "Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "343--348",

booktitle = "Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023",

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Geng, L, Ran, M & Liu, K 2023, Distribution consensus of multi-Agent systems based on model predictive control with probability density function. in Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 343-348, 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023, Qingdao, China, 14/07/23. https://doi.org/10.1109/CFASTA57821.2023.10243316

Distribution consensus of multi-Agent systems based on model predictive control with probability density function. / Geng, Lian; Ran, Maopeng; Liu, Kexin.
Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 343-348 (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ran, Maopeng

AU - Liu, Kexin

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N2 - A distributed output feedback model predictive control approach based on the probability density function method is proposed to realize multi-Agent distribution consensus. Each agent solves the optimal control input by estimating the worst-case local error and perturbation, modeled into a local min-max optimization problem. In the iterative solving process, the agent i will send its information to its neighbor agent through the communication topology, so as to achieve the convergence of group consensus error. Under the assumption of controllability and observability, the proposed control method provides an upper bound for the group distribution consensus error, thus ensuring the practical distribution consensus performance under unmeasured interference and noise.

AB - A distributed output feedback model predictive control approach based on the probability density function method is proposed to realize multi-Agent distribution consensus. Each agent solves the optimal control input by estimating the worst-case local error and perturbation, modeled into a local min-max optimization problem. In the iterative solving process, the agent i will send its information to its neighbor agent through the communication topology, so as to achieve the convergence of group consensus error. Under the assumption of controllability and observability, the proposed control method provides an upper bound for the group distribution consensus error, thus ensuring the practical distribution consensus performance under unmeasured interference and noise.

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KW - Model Predictive Control

KW - Multi-Agent

KW - Probability Density Function

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DO - 10.1109/CFASTA57821.2023.10243316

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BT - Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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Geng L, Ran M, Liu K. Distribution consensus of multi-Agent systems based on model predictive control with probability density function. In Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 343-348. (Proceedings of the 2nd Conference on Fully Actuated System Theory and Applications, CFASTA 2023). doi: 10.1109/CFASTA57821.2023.10243316

Distribution consensus of multi-Agent systems based on model predictive control with probability density function

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