Cooperative coevolutionary algorithm-based model predictive control guaranteeing stability of multirobot formation

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Cooperative coevolutionary algorithm-based model predictive control guaranteeing stability of multirobot formation. / Lee, Seung Mok; Kim, Hanguen; Myung, Hyun; Yao, Xin.

In: IEEE Transactions on Control Systems Technology, Vol. 23, No. 1, 6781597, 01.01.2015, p. 37-51.

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@article{e85a590a744a4d58a110ccab4d483c4d,
title = "Cooperative coevolutionary algorithm-based model predictive control guaranteeing stability of multirobot formation",
abstract = "This paper proposes a novel cooperative coevolutionary algorithm (CCEA)-based distributed model predictive control (MPC) that guarantees asymptotic stability of multiagent systems whose state vectors are coupled and nonseparable in a cost function. While conventional evolutionary algorithm-based MPC approaches cannot guarantee stability, the proposed CCEA-based MPC approach guarantees asymptotic stability regardless of the optimality of the solution that the CCEA-based algorithm generates with a small number of individuals. To guarantee stability, a terminal state constraint is found, and then a repair algorithm is applied to all candidate solutions to meet the constraint. Furthermore, as the proposed CCEA-based algorithm finds the Nash-equilibrium state in a distributed way, robots can quickly move into a desired formation from their locations. A novel dynamic cooperatively coevolving particle swarm optimization (CCPSO), dynamic CCPSO (dCCPSO) in short, is proposed to deal with the formation control problem based on the conventional CCPSO, which was the most recently developed algorithm among CCEAs. Numerical simulations and experimental results demonstrate that the CCEA-based MPC greatly improves the performance of multirobot formation control compared with conventional particle swarm optimization-based MPC.",
keywords = "Cooperative coevolutionary algorithm (CCEA), cooperatively coevolving particle swarm optimization (CCPSO), formation control, model predictive control (MPC), Multirobot.",
author = "Lee, {Seung Mok} and Hanguen Kim and Hyun Myung and Xin Yao",
year = "2015",
month = jan,
day = "1",
doi = "10.1109/TCST.2014.2312324",
language = "English",
volume = "23",
pages = "37--51",
journal = "IEEE Transactions on Control Systems Technology",
issn = "1063-6536",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "1",

}

RIS

TY - JOUR

T1 - Cooperative coevolutionary algorithm-based model predictive control guaranteeing stability of multirobot formation

AU - Lee, Seung Mok

AU - Kim, Hanguen

AU - Myung, Hyun

AU - Yao, Xin

PY - 2015/1/1

Y1 - 2015/1/1

N2 - This paper proposes a novel cooperative coevolutionary algorithm (CCEA)-based distributed model predictive control (MPC) that guarantees asymptotic stability of multiagent systems whose state vectors are coupled and nonseparable in a cost function. While conventional evolutionary algorithm-based MPC approaches cannot guarantee stability, the proposed CCEA-based MPC approach guarantees asymptotic stability regardless of the optimality of the solution that the CCEA-based algorithm generates with a small number of individuals. To guarantee stability, a terminal state constraint is found, and then a repair algorithm is applied to all candidate solutions to meet the constraint. Furthermore, as the proposed CCEA-based algorithm finds the Nash-equilibrium state in a distributed way, robots can quickly move into a desired formation from their locations. A novel dynamic cooperatively coevolving particle swarm optimization (CCPSO), dynamic CCPSO (dCCPSO) in short, is proposed to deal with the formation control problem based on the conventional CCPSO, which was the most recently developed algorithm among CCEAs. Numerical simulations and experimental results demonstrate that the CCEA-based MPC greatly improves the performance of multirobot formation control compared with conventional particle swarm optimization-based MPC.

AB - This paper proposes a novel cooperative coevolutionary algorithm (CCEA)-based distributed model predictive control (MPC) that guarantees asymptotic stability of multiagent systems whose state vectors are coupled and nonseparable in a cost function. While conventional evolutionary algorithm-based MPC approaches cannot guarantee stability, the proposed CCEA-based MPC approach guarantees asymptotic stability regardless of the optimality of the solution that the CCEA-based algorithm generates with a small number of individuals. To guarantee stability, a terminal state constraint is found, and then a repair algorithm is applied to all candidate solutions to meet the constraint. Furthermore, as the proposed CCEA-based algorithm finds the Nash-equilibrium state in a distributed way, robots can quickly move into a desired formation from their locations. A novel dynamic cooperatively coevolving particle swarm optimization (CCPSO), dynamic CCPSO (dCCPSO) in short, is proposed to deal with the formation control problem based on the conventional CCPSO, which was the most recently developed algorithm among CCEAs. Numerical simulations and experimental results demonstrate that the CCEA-based MPC greatly improves the performance of multirobot formation control compared with conventional particle swarm optimization-based MPC.

KW - Cooperative coevolutionary algorithm (CCEA)

KW - cooperatively coevolving particle swarm optimization (CCPSO)

KW - formation control

KW - model predictive control (MPC)

KW - Multirobot.

UR - http://www.scopus.com/inward/record.url?scp=84919596909&partnerID=8YFLogxK

U2 - 10.1109/TCST.2014.2312324

DO - 10.1109/TCST.2014.2312324

M3 - Article

AN - SCOPUS:84919596909

VL - 23

SP - 37

EP - 51

JO - IEEE Transactions on Control Systems Technology

JF - IEEE Transactions on Control Systems Technology

SN - 1063-6536

IS - 1

M1 - 6781597

ER -