Full Text:   <1161>

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CLC number: TP273

On-line Access: 2021-08-17

Received: 2020-04-21

Revision Accepted: 2020-09-04

Crosschecked: 2021-06-08

Cited: 0

Clicked: 2825

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Changyin Sun

https://orcid.org/0000-0001-9269-334X

Jiaqi Li

https://orcid.org/0000-0003-0614-4358

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.8 P.1068-1079

http://doi.org/10.1631/FITEE.2000182


Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach


Author(s):  Jiaqi Li, Qingling Wang, Yanxu Su, Changyin Sun

Affiliation(s):  School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China; more

Corresponding email(s):   jiaqil2018@seu.edu.cn, qlwang@seu.edu.cn, yanxu.su@seu.edu.cn, cysun@seu.edu.cn

Key Words:  Consensus, Self-triggered control, Distributed model predictive control


Jiaqi Li, Qingling Wang, Yanxu Su, Changyin Sun. Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(8): 1068-1079.

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author="Jiaqi Li, Qingling Wang, Yanxu Su, Changyin Sun",
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pages="1068-1079",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000182"
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T1 - Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach
A1 - Jiaqi Li
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Abstract: 
This study investigates the consensus problem of a nonlinear discrete-time multi-agent system (MAS) under bounded additive disturbances. We propose a self-triggered robust distributed model predictive control consensus algorithm. A new cost function is constructed and MAS is coupled through this function. Based on the proposed cost function, a self-triggered mechanism is adopted to reduce the communication load. Furthermore, to overcome additive disturbances, a local minimum– maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each agent. Sufficient conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop MAS. For each agent, we provide a concrete form of compatibility constraint and a consensus error terminal region. Numerical examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.

面向离散多智能体系统一致性问题的自触发鲁棒分布式模型预测控制方法

李佳琦1,王庆领2,苏延旭2,孙长银
1东南大学网络空间安全学院,中国南京市,210096
2东南大学自动化学院,中国南京市,210096
摘要:针对一类有界加性扰动下的非线性离散多智能体系统一致性问题,提出一种基于自触发鲁棒分布式模型预测控制的一致性算法。首先构造了一个新的代价函数,多智能体系统通过该函数进行耦合控制。在该代价函数基础上,采用自触发机制,有效降低了通信负担。为克服加性扰动,利用每个智能体的模型预测控制器迭代求解最坏情况下的局部最小–最大优化问题。然后,给出保证算法迭代可行性和闭环多智能体系统达到一致性的充分条件。对于每个智能体,设计了兼容性约束和一致性误差终端域。最后,通过仿真算例验证了所提算法的有效性和正确性。

关键词:一致性;自触发控制;分布式模型预测控制

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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