TY - GEN
T1 - Optimized Containment for Multiple Unknown Nonlinear Agents via Reinforcement Learning
AU - Miao, Guiqi
AU - Ren, Yatao
AU - Liu, Yongfang
AU - Zhao, Yu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The optimal containment control problem is studied for a series of nonlinear multiagent systems with unknown dynamics in this work. To solve the Hamilton Jacobi-Bellman (HJB) equation associated with the unknown dynamics of agents, a reinforcement learning (RL) approch is used in design of containment algorithm with an actor-critic-identifier (ACI) architecture. The convergence is verified based on Lyapunov analysis methods. Finally, simulation studies are shown to demonstrate the control performance.
AB - The optimal containment control problem is studied for a series of nonlinear multiagent systems with unknown dynamics in this work. To solve the Hamilton Jacobi-Bellman (HJB) equation associated with the unknown dynamics of agents, a reinforcement learning (RL) approch is used in design of containment algorithm with an actor-critic-identifier (ACI) architecture. The convergence is verified based on Lyapunov analysis methods. Finally, simulation studies are shown to demonstrate the control performance.
KW - nonlinear multiagent systems
KW - Optimized containment control
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85218060984&partnerID=8YFLogxK
U2 - 10.1109/ICUS61736.2024.10839793
DO - 10.1109/ICUS61736.2024.10839793
M3 - 会议稿件
AN - SCOPUS:85218060984
T3 - Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
SP - 107
EP - 112
BT - Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
Y2 - 18 October 2024 through 20 October 2024
ER -