文献类型：期刊文献

英文题名：Event-triggered distributed predictive control for asynchronous coordination of multi-agent systems

作者：Zou, Yuanyuan[1];Su, Xu[2];Li, Shaoyuan[1];Niu, Yugang[2];Li, Dewei[1]

机构：[1]Shanghai Jiao Tong Univ, Minist Educ, Dept Automat, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2019

卷号：99

起止页码：92

外文期刊名：AUTOMATICA

收录：;EI(收录号：20184506037321);WOS:【SCI-EXPANDED(收录号:WOS:000454965300010)】；

基金：This paper was supported by the National Natural Science Foundation of China under Grants (61773162, 61590924, 61833012 and 61673174). The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Giancarlo Ferrari-Trecate under the direction of Editor Ian R. Petersen.

语种：英文

外文关键词：Distributed predictive control (DPC); Event-triggered mechanism; Asynchronous coordination; Multi-agent systems

摘要：This paper investigates the event-triggered distributed predictive control (DPC) problem for multi agent systems subject to bounded disturbances. A novel event-triggering mechanism which involves the neighbours' information is derived for each agent to achieve a trade-off between resource usage and control performance. In such a framework, the DPC optimization problem is solved and information is exchanged only at triggering instants, thus achieving asynchronous coordination. To lower computation and communication consumption more significantly, a dynamic variable considering effects of neighbours is introduced to design a dynamic event-triggering condition and we show that larger inter-execution time can be obtained using the dynamic triggering mechanism. The theoretical conditions on ensuring feasibility and closed-loop stability are developed for these two triggering mechanisms, respectively. Finally, numerical simulations are given to illustrate the effectiveness of the proposed control strategy. (C) 2018 Elsevier Ltd. All rights reserved.