Fixed-time cluster consensus for multi-agent systems with objective
optimization on directed networks
Abstract
This paper studies the cluster consensus of multi-agent systems (MASs)
with objective optimization on directed and detail balanced networks, in
which the global optimization objective function is a linear combination
of local objective functions of all agents. Firstly, a directed and
detail balanced network is constructed that depends on the weights of
the global objective function. Secondly, two new continuous-time
optimization algorithms are proposed based on time-invariant and
time-varying cost functions to ensure that all agents reach cluster
consensus within a fixed-time, and the global objective function
asymptotically reaches the optimal solution. Finally, two examples are
presented to show the efficacy of the theoretical results.