International Journal of Robust and Nonlinear Control,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 11, 2024
Abstract
This
article
investigates
a
formation
control
method
for
stochastic
nonlinear
multi‐agent
systems
(MASs)
under
switching
topologies.
To
reduce
the
communication
bandwidth
occupancy,
two
event‐triggered
mechanisms
of
sensor‐to‐controller
and
controller‐to‐actuator
network
channels
are
proposed.
Taking
advantage
neural
networks
approximation
capability,
dynamic
high‐gain
observer
is
introduced
to
estimate
unmeasured
states
tackle
non‐differentiable
issue
triggered
output
signal.
Furthermore,
it
should
be
noted
that
distributed
filter
employed
handle
discontinuous
local
reference
signal
resulting
from
By
using
topology
information,
generates
differentiable
design
virtual
controller.
Concomitantly,
first‐order
implemented
avoid
problem
“explosion
complexity.”
Through
stability
analysis,
proven
designed
controller
achieves
boundedness
in
probability
all
signals
MASs.
Ultimately,
simulation
performed
confirm
viability
approach.
IEEE Transactions on Neural Networks and Learning Systems,
Journal Year:
2024,
Volume and Issue:
36(3), P. 5334 - 5347
Published: April 3, 2024
This
article
proposes
a
dimensionality
reduction
approach
to
study
the
output
regulation
problem
(ORP)
of
Boolean
control
networks
(BCNs),
which
has
much
lower
computational
complexity
than
previous
results.
First,
an
auxiliary
system
is
smaller
in
scale
augmented
constructed.
By
analyzing
set
stabilization
as
well
original
BCN,
necessary
and
sufficient
condition
detect
solvability
ORP
presented.
Second,
method
design
state
feedback
controls
for
proposed.
Finally,
two
biological
examples
are
given
demonstrate
effectiveness
advantage
obtained
new
International Journal of Systems Science,
Journal Year:
2023,
Volume and Issue:
55(3), P. 391 - 406
Published: Oct. 25, 2023
This
paper
aims
to
realise
the
robust
output
bipartite
consensus
for
unknown
heterogeneous
linear
time-varying
multiagent
systems
(MASs)
subject
varying
trial
lengths,
measurement
disturbances
and
data
quantisation.
To
this
end,
inspired
by
idea
of
quantised
control,
a
data-driven
adaptive
iterative
learning
(AILBC)
method
is
proposed.
Specifically,
address
problem
distributed
auxiliary
prediction
system
constructed
based
on
agents'
input-output
(I/O)
dynamic
relationship.
An
update
protocol
developed
estimate
parameters
I/O
Subsequently,
control
(ILC)
approach
information
proposed
MASs
achieve
tracking,
with
an
attempt
relax
need
explicit
model
information.
The
tracking
errors
are
ultimately
bounded
through
rigorous
analysis,
result
further
extended
switching
topologies.
Finally,
numerical
simulations
conducted
verify
validity
AILBC
method.
International Journal of Robust and Nonlinear Control,
Journal Year:
2023,
Volume and Issue:
34(5), P. 3318 - 3334
Published: Dec. 15, 2023
Abstract
This
paper
proposes
a
data‐driven
bipartite
leader‐following
consensus
strategy
for
class
of
nonlinear
multi‐agent
systems
(MASs)
under
external
disturbances
and
hybrid
attacks,
which
are
composed
denial‐of‐service
attacks
false
data
injection
attacks.
algorithm
incorporates
no
system
dynamics
only
utilizes
the
input
output
generated
by
controlled
plant.
First,
MAS
with
can
be
transformed
into
an
equivalent
linear
model
applying
revised
dynamic
linearization
method.
Second,
hybrid‐attack
compensation
mechanism
is
proposed
to
alleviate
adverse
impact
dropout
caused
Then,
based
on
mechanism,
extended
state
observer
designed
that
mitigate
negative
influence
induced
improve
control
performance
even
though
threatened
The
still
remain
stable
strategy.
Finally,
simulation
examples
demonstrate
validity
strategy,
error
reduced
small
range.
Symmetry,
Journal Year:
2024,
Volume and Issue:
16(4), P. 426 - 426
Published: April 3, 2024
This
article
studies
an
event-triggered
fixed-time
trajectory
tracking
control
problem
of
n-joint
manipulator
system.
Firstly,
a
disturbance
observer
is
proposed
to
reconstruct
the
total
composed
external
disturbances
and
model
uncertainties,
using
estimation
as
feedforward
compensation
enhance
system
robustness.
Subsequently,
based
on
backstepping
framework,
controller
with
event-triggering
mechanism
designed
for
ensure
convergence
errors
zero
within
fixed
time.
Additionally,
two
conditions
are
devised
reduce
transmission
time
input
computation
output.
Simultaneously,
Zeno
behavior
excluded
through
theoretical
proof,
validating
stability
closed-loop
Finally,
simulation
verification
conducted
two-joint
manipulator,
results
confirming
effectiveness
strategy.
