Distributed Model Predictive Control for Linear-Quadratic Performance and Consensus State Optimization of Multiagent Systems

被引：41
|
作者：
Wang, Qishao [1 ]
Duan, Zhisheng [2 ]
Lv, Yuezu [3 ]
Wang, Qingyun [1 ]
Chen, Guanrong [4 ]
机构：
[1] Beihang Univ, Dept Dynam & Control, Beijing 100191, Peoples R China
[2] Peking Univ, Coll Engn, Beijing 100871, Peoples R China
[3] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China
[4] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2021年 / 51卷 / 06期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Multi-agent systems; Optimization; Prediction algorithms; Predictive control; Performance analysis; Transient response; Transient analysis; Asynchronous sampling; distributed optimization; model predictive control (MPC); multiagent systems; TIME CONVEX-OPTIMIZATION; CONSTRAINTS; ALGORITHMS;
D O I：
10.1109/TCYB.2020.3001347
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The optimal consensus problem of asynchronous sampling single-integrator and double-integrator multiagent systems is solved by distributed model predictive control (MPC) algorithms proposed in this article. In each predictive horizon, the finite-time linear-quadratic performance is minimized distributively by the control input with consensus state optimization. The MPC technique is then utilized to extend the optimal control sequence to the case of an infinite horizon. Conditions depending only on each agent's weighting scalar and sampling step are derived to guarantee the stability of the closed-loop system. Numerical examples of rendezvous control of multirobot systems illustrate the efficiency of the proposed algorithm.
引用
收藏
页码：2905 / 2915
页数：11
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