Asian Journal of Control,
Journal Year:
2024,
Volume and Issue:
unknown
Published: July 7, 2024
Abstract
This
work
presents
a
resilient
distributed
model
predictive
control
(MPC)
method
for
linear
parameter
varying
(LPV)
systems
with
state
delays
and
attacks
in
communication
networks.
Coordinations
are
required
MPC
(DMPC)
to
achieve
the
global
performance
of
centralized
(CMPC).
However,
can
be
severely
degraded
by
unreliable
networks,
example,
denial
service
(DoS)
attacks.
A
framework
is
derived
address
communications
DMPC.
system
divided
into
subsystems
purpose.
To
deal
uncertainties
delays,
“min‐max”
DMPC
algorithm
presented
buffer
ensure
resilience
against
DoS
quantization
scheme
introduced
quantize
information
exchanged
between
subsystems.
An
iterative
interaction
proposed
exchange
feedback
laws
among
The
stability
closed‐loop
under
ensured
using
Lyapunov
function
method.
effectiveness
demonstrated
through
two
simulation
examples.
Asian Journal of Control,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 14, 2025
Abstract
In
this
paper,
a
min–max
tracking
model
predictive
control
(MPC)
method
for
linear
parameter‐varying
(LPV)
systems
using
polyhedral
invariant
sets
is
proposed.
The
aims
to
expand
the
error
state
stabilizable
domain
and
improve
dynamic
performance
while
handling
asymmetric
system
constraints
guaranteeing
robust
stability
under
parameter
uncertainty,
with
low
computational
burden.
Firstly,
augmented
formulation
constructed
based
on
original
state‐space
reference
trajectory
obtain
state.
Secondly,
optimization
problem
considering
variation
formulated
Thirdly,
sequence
of
optimal
laws
offline
obtained
by
solving
design
nested
corresponding
sets.
These
have
larger
than
ellipsoidal
An
interpolation
applied
during
online
control.
Finally,
simulation
results
comparative
analysis
substantiate
effectiveness
proposed
MPC
method.
Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 28, 2025
Tunnel
boring
machine
(TBM)
is
a
safe
and
effective
equipment
for
excavating
tunnels.
The
advance
control
of
the
driving
cutterhead
system
plays
an
important
role
in
hard
rock
TBM
excavation.
This
work
presents
robust
model
predictive
(MPC)
optimizing
torques
motors.
First,
established
with
state-space
representation
subject
to
constraints
additional
disturbances.
Based
on
real
operating
data,
parameters
are
identified
by
using
prediction
error
method.
To
address
robustness
issue,
output
disturbance
constructed.
states
estimated
state
feedback
design
Kalman
filter.
state,
MPC
designed
presenting
compensation
strategy
considered
together.
Extensive
simulations
based
given
test
performance
under
tracking
rejection
system.
Water Resources Research,
Journal Year:
2025,
Volume and Issue:
61(4)
Published: April 1, 2025
Abstract
Recent
developments
in
control
theory,
coupled
with
the
growing
availability
of
real‐time
data,
have
paved
way
for
improved
data‐driven
methodologies.
This
study
explores
application
Data‐Enabled
Predictive
Control
(DeePC)
algorithm
to
optimize
operation
water
distribution
systems
(WDS).
WDS
are
characterized
by
inherent
uncertainties
and
complex
nonlinear
dynamics.
Hence,
classic
strategies
involving
physical
model‐based
or
state‐space
methods
often
difficult
implement
scale.
The
DeePC
method
suggests
a
paradigm
shift
utilizing
approach.
technique
employs
finite
set
input‐output
samples
(control
settings
measured
data)
learn
an
unknown
system's
behavior
derive
optimal
policies,
effectively
bypassing
need
explicit
mathematical
model
system.
In
this
study,
is
applied
two
applications
pressure
management
chlorine
disinfection
scheduling,
demonstrating
superior
performance
compared
standard
strategies.
AIChE Journal,
Journal Year:
2024,
Volume and Issue:
71(3)
Published: Dec. 11, 2024
Abstract
Data‐enabled
predictive
control
(DeePC)
is
a
data‐driven
algorithm
that
utilizes
data
matrices
to
form
non‐parametric
representation
of
the
underlying
system,
predicting
future
behaviors
and
generating
optimal
actions.
DeePC
typically
requires
solving
an
online
optimization
problem,
complexity
which
heavily
influenced
by
amount
used,
potentially
leading
expensive
computation.
In
this
article,
we
leverage
deep
learning
propose
highly
computationally
efficient
approach
for
general
nonlinear
processes,
referred
as
Deep
DeePC.
Specifically,
neural
network
employed
learn
vector
operator,
essential
component
This
trained
offline
using
historical
open‐loop
input
output
process.
With
network,
framework
formed
implementation.
At
each
sampling
instant,
directly
outputs
eliminating
need
conventional
The
action
obtained
based
on
operator
updated
network.
To
address
constrained
scenarios,
constraint
handling
scheme
further
proposed
integrated
with
handle
hard
constraints
during
efficacy
superiority
are
demonstrated
two
benchmark
process
examples.
AIChE Journal,
Journal Year:
2024,
Volume and Issue:
71(2)
Published: Nov. 22, 2024
Abstract
In
this
article,
we
propose
a
physics‐informed
learning‐based
Koopman
modeling
approach
and
present
Koopman‐based
self‐tuning
moving
horizon
estimation
design
for
class
of
nonlinear
systems.
Specifically,
train
operators
two
neural
networks—the
state
lifting
network
the
noise
characterization
network—using
both
data
available
physical
information.
The
first
accounts
functions
model,
while
second
characterizes
system
distributions.
Accordingly,
stochastic
linear
model
is
established
in
lifted
space
to
forecast
dynamic
behaviors
system.
Based
on
(MHE)
scheme
developed.
weighting
matrices
MHE
are
updated
using
pretrained
at
each
sampling
instant.
proposed
computationally
efficient,
as
only
convex
optimization
needs
be
solved
during
online
implementation,
updating
does
not
require
re‐training
networks.
We
verify
effectiveness
evaluate
performance
method
via
application
simulated
chemical
process.
