TY - JOUR
T1 - Layered Affine Formation Control of Networked Uncertain Systems
T2 - A Fully Distributed Approach over Directed Graphs
AU - Li, Dongyu
AU - Ma, Guangfu
AU - Xu, Yang
AU - He, Wei
AU - Ge, Shuzhi Sam
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Distributed formation control is presented for networked Euler-Lagrange systems (ELSs) over a directed interaction topology. This problem is defined by a layered framework in which information flow both among the leaders and among the followers is described by different layers. To empower the formation to make a variety of geometric transformations, we present the necessary and sufficient conditions for affine maneuverability under a directed graph. Unlike most existing results using a diagonal stabilizing matrix to achieve the stabilizability of affine formation, this fully distributed approach is feasible without any global information. Next, we propose an adaptive control law for agents in each layer, where the closed-loop errors are driven to a neighborhood of the origin in finite time. Adaptive neural networks are integrated to tackle the model uncertainties in ELSs by updating the norm of the weight matrix, which can simplify the control design and alleviate the computational burden compared with traditional ones. The simulation results are given to show the effectiveness of the proposed approach.
AB - Distributed formation control is presented for networked Euler-Lagrange systems (ELSs) over a directed interaction topology. This problem is defined by a layered framework in which information flow both among the leaders and among the followers is described by different layers. To empower the formation to make a variety of geometric transformations, we present the necessary and sufficient conditions for affine maneuverability under a directed graph. Unlike most existing results using a diagonal stabilizing matrix to achieve the stabilizability of affine formation, this fully distributed approach is feasible without any global information. Next, we propose an adaptive control law for agents in each layer, where the closed-loop errors are driven to a neighborhood of the origin in finite time. Adaptive neural networks are integrated to tackle the model uncertainties in ELSs by updating the norm of the weight matrix, which can simplify the control design and alleviate the computational burden compared with traditional ones. The simulation results are given to show the effectiveness of the proposed approach.
KW - Adaptive neural networks
KW - Euler-Lagrange systems (ELSs)
KW - layered formation control
UR - http://www.scopus.com/inward/record.url?scp=85122215411&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2020.2965657
DO - 10.1109/TCYB.2020.2965657
M3 - 文章
C2 - 32031959
AN - SCOPUS:85122215411
SN - 2168-2267
VL - 51
SP - 6119
EP - 6130
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 12
ER -