TY - JOUR
T1 - Antagonistic Interaction-Based Bipartite Consensus Control for Heterogeneous Networked Systems
AU - Liu, Guangliang
AU - Liang, Hongjing
AU - Pan, Yingnan
AU - Ahn, Choon Ki
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62073046 and Grant 62003052, and in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (Ministry of Science and ICT) under Garnt NRF-2020R1A2C1005449.
Publisher Copyright:
© 2013 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - This article investigates the bipartite consensus tracking control problem for nonlinear networked systems with antagonistic interactions and unknown backlash-like hysteresis. The generalized networked multiagent systems model is considered, in which every agent is an independent individual, and this model allows competitive and cooperative interactions to coexist. A Gaussian function is applied to simulate competition and cooperation among agents. Radial basis function (RBF) neural network (NN) is applied to estimate the unknown nonlinear function. By using backstepping technology, we propose an adaptive neural control protocol, which not only ensures that in the closed-loop system all the signals are bounded but also realizes bipartite consensus control. Finally, we present a simulation example to illustrate the effectiveness of the obtained result.
AB - This article investigates the bipartite consensus tracking control problem for nonlinear networked systems with antagonistic interactions and unknown backlash-like hysteresis. The generalized networked multiagent systems model is considered, in which every agent is an independent individual, and this model allows competitive and cooperative interactions to coexist. A Gaussian function is applied to simulate competition and cooperation among agents. Radial basis function (RBF) neural network (NN) is applied to estimate the unknown nonlinear function. By using backstepping technology, we propose an adaptive neural control protocol, which not only ensures that in the closed-loop system all the signals are bounded but also realizes bipartite consensus control. Finally, we present a simulation example to illustrate the effectiveness of the obtained result.
KW - Antagonistic interactions
KW - bipartite consensus control
KW - heterogeneous networked systems
KW - neural networks (NNs)
KW - unknown backlash-like hysteresis
UR - http://www.scopus.com/inward/record.url?scp=85129358883&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2022.3167120
DO - 10.1109/TSMC.2022.3167120
M3 - Article
AN - SCOPUS:85129358883
SN - 2168-2216
VL - 53
SP - 71
EP - 81
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 1
ER -