TY - JOUR
T1 - Distributed Model Predictive Consensus With Self-Triggered Mechanism in General Linear Multiagent Systems
AU - Zhan, Jingyuan
AU - Jiang, Zhong Ping
AU - Wang, Yebin
AU - Li, Xiang
N1 - Funding Information:
Manuscript received August 20, 2018; accepted November 21, 2018. Date of publication December 3, 2018; date of current version July 3, 2019. This work was supported by the National Natural Science Foundation of China (Nos. 61751303, 71731004, 61803007), by the National Science Fund for Distinguished Young Scholars of China (No. 61425019), by the Rail Transit Joint Funds of Beijing Natural Science Foundation and Traffic Control Technology (No. L171001), by the U.S. National Science Foundation grant ECCS-1501044, and by the Mit-subishi Electric Research Laboratories. Paper no. TII-18-2165. (Corresponding author: Xiang Li.) J. Zhan was with the Adaptive Networks and Control Lab, Department of Electronic Engineering, the Research Center of Smart Networks and Systems, School of Information Science and Engineering, Fudan University Shanghai 200433. She is now with Beijing Key Laboratory of Transportation Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China (e-mail:, [email protected]).
Funding Information:
This work was supported by the National Natural Science Foundation of China (Nos. 61751303, 71731004, 61803007), by the National Science Fund for Distinguished Young Scholars of China (No. 61425019), by the Rail Transit Joint Funds of Beijing Natural Science Foundation and Traffic Control Technology (No. L171001), by the U.S. National Science Foundation grant ECCS-1501044, and by the Mitsubishi Electric Research Laboratories. Paper no. TII-18-2165.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This paper investigates the consensus problem of general linear discrete-time multiagent systems by using distributed model predictive control (DMPC) with self-triggered mechanism. First, a novel DMPC-based consensus algorithm is proposed, where each agent only needs to obtain its neighbors' predicted state sequences once at each time step. We prove that the resultant DMPC optimization problem is feasible, and the proposed algorithm guarantees the dynamic consensus of agents. Then, to further reduce the communication cost and the energy consumption of control updates, a self-triggered DMPC-based consensus algorithm is proposed with the control input and the triggering interval jointly optimized. Numerical examples including the benchmark problem with platooning vehicles are provided to verify the effectiveness and advantages of the proposed algorithms.
AB - This paper investigates the consensus problem of general linear discrete-time multiagent systems by using distributed model predictive control (DMPC) with self-triggered mechanism. First, a novel DMPC-based consensus algorithm is proposed, where each agent only needs to obtain its neighbors' predicted state sequences once at each time step. We prove that the resultant DMPC optimization problem is feasible, and the proposed algorithm guarantees the dynamic consensus of agents. Then, to further reduce the communication cost and the energy consumption of control updates, a self-triggered DMPC-based consensus algorithm is proposed with the control input and the triggering interval jointly optimized. Numerical examples including the benchmark problem with platooning vehicles are provided to verify the effectiveness and advantages of the proposed algorithms.
KW - Consensus
KW - distributed model predictive control (DMPC)
KW - multiagent system
KW - self-triggered control
UR - http://www.scopus.com/inward/record.url?scp=85057862297&partnerID=8YFLogxK
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U2 - 10.1109/TII.2018.2884449
DO - 10.1109/TII.2018.2884449
M3 - Article
AN - SCOPUS:85057862297
SN - 1551-3203
VL - 15
SP - 3987
EP - 3997
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 7
M1 - 8556099
ER -