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

Qishao Wang; Zhisheng Duan; Yuezu Lv; Qingyun Wang; Guanrong Chen

doi:10.1109/TCYB.2020.3001347

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

Qishao Wang
, Zhisheng Duan^*
, Yuezu Lv
, Qingyun Wang
, Guanrong Chen

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

58 Citations (Scopus)

Abstract

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.

Original language	English
Article number	9133446
Pages (from-to)	2905-2915
Number of pages	11
Journal	IEEE Transactions on Cybernetics
Volume	51
Issue number	6
DOIs	https://doi.org/10.1109/TCYB.2020.3001347
Publication status	Published - Jun 2021
Externally published	Yes

Keywords

Asynchronous sampling
distributed optimization
model predictive control (MPC)
multiagent systems

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10.1109/TCYB.2020.3001347

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abstract = "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.",

keywords = "Asynchronous sampling, distributed optimization, model predictive control (MPC), multiagent systems",

author = "Qishao Wang and Zhisheng Duan and Yuezu Lv and Qingyun Wang and Guanrong Chen",

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AU - Wang, Qishao

AU - Duan, Zhisheng

AU - Lv, Yuezu

AU - Wang, Qingyun

AU - Chen, Guanrong

PY - 2021/6

Y1 - 2021/6

N2 - 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.

AB - 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.

KW - Asynchronous sampling

KW - distributed optimization

KW - model predictive control (MPC)

KW - multiagent systems

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JF - IEEE Transactions on Cybernetics

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M1 - 9133446

ER -

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

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Keywords

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