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

Jiaqi LI; Qingling WANG; Yanxu SU; Changyin SUN

doi:10.1631/FITEE.2000182

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

Regular Papers | Updated：2022-05-19

- Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 8, Pages: 1068-1079(2021)
- Affiliations：
  
  School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
  School of Automation, Southeast University, Nanjing 210096, China
- Author bio：
  
  [ "Jiaqi LI, E-mail: jiaqil2018@seu.edu.cn" ]
  [ "Qingling WANG, E-mail: qlwang@seu.edu.cn" ]
  [ "Yanxu SU, E-mail: yanxu.su@seu.edu.cn" ]
  Changyin SUN, E-mail: cysun@seu.edu.cn
- Funds：
- DOI：10.1631/FITEE.2000182
  CLC： TP273
- Received：21 April 2020，
  
  Revised：2021-;6-;8，
  
  Published：2021-08
- Accepted：
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Jiaqi LI, Qingling WANG, Yanxu SU, et al. 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.
DOI：

Jiaqi LI, Qingling WANG, Yanxu SU, et al. 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. DOI： 10.1631/FITEE.2000182.

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。

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.

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