工业无线传感器网络的丢包下分布式估计与控制融合策略
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本文深入探讨了"工业无线传感器和执行器网络的带丢包的分布式估计融合和控制"这一主题，针对工业自动化控制领域中的混合两层IWSAN系统提出了创新性的解决方案。在哈希工业环境中，无线通信因其实时性和可靠性要求而面临严峻挑战。研究者们认识到，为了确保工业自动化过程的高效运作，必须处理无线通信中的数据丢包问题。
首先，作者提出了一种交互式对偶模型（IDM）自适应估计算法，该算法的核心在于其能够动态地识别并适应无线通信信道的不稳定性和丢包现象。这个模型通过实时调整估计策略，提高了系统的鲁棒性，确保在数据不完整的情况下仍能进行有效的估计。
接着，文章强调了联合多传感器估计融合的重要性，特别是在处理复杂工业环境中可能存在的多种传感器数据时。通过这种方法，多个传感器的数据被有效地整合，不仅提高了状态估计的准确性，还增强了系统的容错能力，即使个别传感器出现故障，也能保持整体性能。
考虑到IWSAN的广阔地理部署需求，分布式协作控制策略也被纳入考虑。这种策略允许网络中的各个节点协同工作，共同完成任务，进一步提升了整个系统的稳定性和效率。通过分布式控制，即便在网络部分节点发生故障或通信中断时，其他节点仍然可以继续执行任务，从而降低了整体系统对单一节点的依赖。
以温度控制为例，研究者展示了他们的方法如何在实际应用中有效应对丢包问题，通过实时监控和动态调整，实现了对工业环境的精确控制，证明了这一融合和控制策略的有效性和实用性。
这篇研究论文不仅解决了工业无线传感器网络中的关键问题，还为工业自动化领域的无线通信和控制提供了重要的理论支持和技术手段，对于推动工业4.0时代的智能制造具有重要意义。
International Journal of Distributed Sensor Networks
2.1. Graph of Hybrid Two-Tier IWSANs. In order to facilitate
the following work, the communication relationship between
the actuators and sensors and among actuators themselves
over the hybrid two-tier IWSANs can be described by a graph
G. Assume that there are cells in a workshop. An actuator
(i.e., central air conditioning) and a group of temperature
sensors with wireless communication function are deployed
within every cell. At rst, we focus on the graph of actuator
network G
𝑎
{A,E} (i.e., a subgraph of G), where A =
{
1
,...,
𝑛
}is the set of actuators and
E =
𝑖
,
𝑙
|
𝑖
,
𝑙
∈A,
𝑙
∈N
𝑎
𝑖
,∀,∈
{
1,...,
}
()
is the edge set. N
𝑎
𝑖
is a set of the neighbor actuators of
𝑖
.
Each actuator is only allowed to communicate with neighbor
actuators over actuator network, that is, ∃(
𝑖
,
𝑙
)∈E,ifand
only if
𝑙
∈N
𝑎
𝑖
.
Consider that
𝑖
can communicate with a group of
intracell sensors (i.e., S
𝑖
={
1
𝑖
,...,
𝑚
𝑖
})oversensornetwork.
us, the overall graph G can be obtained via taking the
subgraph G
𝑎
and adding ×new vertices S,whereS =
1
∪
2
,...,∪
𝑛
corresponds to the set of all sensors. Dene
the edge set
E
O
=
𝑗
𝑖
,
𝑖
|
𝑖
∈A,
𝑗
𝑖
∈S
𝑖
,∀∈
{
1,...,
}
,
∀∈
{
1,...,
}
.
()
en, we obtain G ={A ∪S,E ∪E
O
}.
Remark 1. In this paper, E
O
represents the radioconnectivity
between actuators and their intracell sensors. However, E
represents not only the radio connectivity but also interactive
acting among neighboring actuators through some ventila-
tion pipes.
2.2. Modeling. Consider the above-mentioned temperature
control system, where the temperature is controlled in an
industrial workshop using hybrid two-tier IWSANs as pre-
sented in Figure . We focus on the following discrete-time
plant model:
𝑖,𝑘+1
=
𝑖
𝑖,𝑘
+
𝑖𝑖
𝑎
𝑖,𝑘
+
𝑎
𝑙
∈N
𝑎
𝑖
𝑖𝑙
𝑎
𝑙,𝑘
,
𝑖,𝑘
=
𝑖
𝑖,𝑘
,
()
where
𝑖,𝑘
, ∈ {1,...,}is the temperature state of th
celldomainofplantattime and is measured by a group
of intracell sensors, that is, S
𝑖
={
1
𝑖
,...,
𝑚
𝑖
},
𝑖,𝑘
=
[
1
𝑖,𝑘
,...,
𝑚
𝑖,𝑘
]
𝑇
is the generalized measurement value of
𝑖,𝑘
collected by S
𝑖
and can be sent to actuator
𝑖
,and
𝑎
𝑖,𝑘
and
𝑎
𝑙,𝑘
, ∈{1,...,}, are the input (output wind temperature)
corresponding to the acting applied to the state
𝑖,𝑘
by local
actuator
𝑖
and neighbor actuator
𝑙
,respectively.
Due to the unreliable wireless communication channels
and the interference from the harsh industrial environment,
it is necessary to take into account a more “realistic” plant
model. First, consider the unreliable wireless communica-
tion channels within actuator network. Let I.I.D Bernoulli
stochastic variable
𝑖𝑙,𝑘
with E{
𝑖𝑙,𝑘
}=
𝑖𝑙
indicate whether
data packet (including state information) from
𝑖
is received
(
𝑖𝑙,𝑘
=1)orlost(
𝑖𝑙,𝑘
=0)byneighbor
𝑙
∈N
𝑎
𝑖
.Apparently,
𝑖𝑖,𝑘
≡1,forall ∈ {1,...,}.Aeranyneighbor
𝑙
of
𝑖
obtains the state information of
𝑖
,itscontrolunitcan
compute an appropriate input to
𝑖
. erefore, when
𝑙
∈N
𝑎
𝑖
and
𝑖𝑙,𝑘
=1,
𝑖,𝑘
will be actuated cooperatively by local
actuator
𝑖
and its neighbor actuators {
𝑙
}.Forinstance,in
Figure ,exceptlocal
1
, cell domain can also receive the
other inputs sent from
2
,
3
,and
4
through some ventilation
pipes, respectively.
For a concise derivation, according to the zero-input
strategy in the literature [], we can write
𝑎
𝑖,𝑘
=
𝑖,𝑘
,
𝑎
𝑙,𝑘
=
𝑙,𝑘
,
()
where
𝑖,𝑘
and
𝑙,𝑘
are the desired control laws computed by
embedded control units of
𝑖
and
𝑙
,respectively.
Secondly,weusetheGilbert-Elliottmodeltosimulate
the unreliable wireless channels within sensor network. At
time , the stochastic variable
𝑖,𝑘
is used to describe the
packet loss model
0
𝑖,𝑘
and the packet arrival model
1
𝑖,𝑘
of
communication channel from S
𝑖
to
𝑖
,forall∈{1,...,},
where
𝛼
𝑖,𝑘
{
𝑖,𝑘
=}, =0,1.Tocapturethetemporal
correlation of channel variation,
𝑖,𝑘
can be modeled as a
two-state Markovian chain with the initial model probability
vector
𝑖,0
and the transition probability matrix (TPM) Ξ
𝑖
as
𝑖,0
=
0
𝑖,0
1
𝑖,0
, ()
Ξ
𝑖
=
00
𝑖
01
𝑖
10
𝑖
11
𝑖
, ()
where
𝛼
𝑖,0
Pr{
𝛼
𝑖,0
} ( = 0,1) is the initial model
probability,
𝛼𝛽
𝑖
Pr{
𝛽
𝑖,𝑘
|
𝛼
𝑖,𝑘−1
} (, = 0,1) is the
transition probability of switching from model
𝛼
𝑖,𝑘−1
to model
𝛽
𝑖,𝑘
,and
00
𝑖
,
11
𝑖
,
01
𝑖
=1−
00
𝑖
,and
10
𝑖
=1−
11
𝑖
are
called packet remaining loss rate, packet remaining arrival
rate, packet recovery rate, and packet failure rate, respectively.
Apparently, Ξ
𝑖
, the TPM, is characterized by the
00
𝑖
and
11
𝑖
.
When aggregating all states at time into the
𝑘
=
[
𝑇
1,𝑘
,...,
𝑇
𝑛,𝑘
]
𝑇
and considering packet losses, noises, and
(), the above plant model () can be written in vector form
as
𝑘+1
=
𝑘
+
𝑘
𝑘
+
𝑘
,
𝑘
=
𝑘
𝑘
+]
𝑘
,
()
where
𝑘
∈ R
𝑛
,
𝑘
∈ R
𝑛
,
𝑘
∈ R
𝑛𝑚
,
𝑘
∈ R
𝑛
, ]
𝑘
∈ R
𝑛𝑚
,
𝑘
and ]
𝑘
are mutually uncorrelated Gaussian, white, and zero-
mean noises with covariance >0and covariance >0,
respectively, =diag(
1
,...,
𝑛
), (
𝑘
)∈R
𝑛×𝑛
,
𝑖𝑙
(
𝑖𝑙,𝑘
)=
0if
𝑙
∉N
𝑎
𝑖
or
𝑖𝑙,𝑘
=0,and(
𝑘
)=diag(
1
(
𝑘
),...,
𝑛
(
𝑘
))
with
𝑖
(
𝑘
)=[
1
𝑖
(
𝑖,𝑘
),...,
𝑚
𝑖
(
𝑖,𝑘
)]
𝑇
.
In this paper, the main objective is to control temperature
within every cell domain to a required set value. For the
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