Chengsen Song
Dates:
Aug
10
thru
Aug
1
2
, 2010
Research
Report Form
A.
Goals
1.
Read paper papers
[1

6]
on delay analysis on NCS.
Find what has been done in delay analysis.
2.
Take notes on t
hese papers.
B.
Accomplishments
1.
Read [
1
,
3
]
.
[1] discussed the LQR controller design. [3] discussed the compensation of delays in
NCSs, which use a model

based estimator
(
ARMAX model
)
.
2.
Find a lecture note on LQG/LQR controller design. Read it.
C.
What I have l
earned
1.
Learned the
basic
concept of LQR controller design
.
D.
New Questions & Future Goals
1.
System performance can be analyzed by showing the diagrams of the experiment results. But
h
ow to analyze the stability of the system?
References
[1] Li

zhen Wu and Xiao

hong Hao. A novel optimal controller design and evaluation for networked
control systems with time

variant delays. Presented at Measuring Technology and Mechatronics
Automation (ICMTMA), 2010 International Conference on.
[2] Guo

Pi
ng Liu, Yuanqing Xia, David Rees and Wenshan Hu. (2007, Design and stability criteria of
networked predictive control systems with random network delay in the feedback channel.
Systems,
Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 37(2),
pp. 173

184.
[3] E. C. Martins and F. G. Jota. (2010, Design of networked control systems with explicit compensation
for time

delay variations.
Systems, Man, and
Cybernetics, Part C: Applications and Reviews, IEEE
Transactions on 40(3),
pp. 308

318.
[4] L. Samaranayake, M. Leksell and S. Alahakoon. Relating sampling period and control delay in
distributed control systems. Presented at Computer as a Tool, 2005. EUR
OCON 2005.the International
Conference on.
[5] R. A. Gupta and Mo

Yuen Chow. (2010, Networked control system: Overview and research trends.
Industrial Electronics, IEEE Transactions on 57(7),
pp. 2527

2535.
[6] Jin Wu, Fei

Qi Deng and Jing

Guang Gao. Mod
eling and stability of long random delay networked
control systems. Presented at Machine Learning and Cybernetics, 2005. Proceedings of 2005
International Conference on.
Notes
1 Advantage of NCSs
NCSs have the features of reduced system wiring, simpler
installation, ease of system diagnosis and
maintenance, increased system edibility.
Many control issues that occur in conventional control systems, such as network delay and data
dropout, sampling, and transmitting methods.
2 NCS model
2.1 SISO
The genera
l sketch of the NCSs is showed in Figure 1 [6].
If the plant is modeled as single input single
output, t
he delay can be categorized as the forward delay (
) and the feedback delay (
). To make
problem simple,
s
ome paper
[2]
only consider the feedback de
lay, and ignore the forward delay. Some
paper
discussed the effect of
both
delays
.
2.2 MIMO
More generally, a class of real MMO NCS with many independent sensors and actuators are considered
[1]. In this system, the delay between the plant and controlle
rs may vary.
3 delay effects
3.1 Modeling and
Analysis
of Network Delays
In general, there are three models of delays in NCS [4]:
1 If it is constant, the plant can be represented in state space using the present input and the past inputs
and the standar
d linear time invariant control theory can be applied.
2 If it is variable and still the delay statistics of the
network are available and has a finite number of delay
states, then it can be treated as a jump linear system driven by an underlying Markov Ch
ain
.
3 If no such data is available or if the delay statistics of the states of the Markov chain does not remain
constant, then either robust controller techniques or predictor

based methods can be
applied
.
Most of the above approaches are feasible only if
the control delay does not exceed the sampling
interval
.
3.2 Delay Compensation[5]
Different mathematical

, heuristic

, and statistical

based approaches are taken for delay compensation
in NCSs, including t
he optimal stochastic method, the
queuing/buffering method, the robust control
method.
[2] proposed a control strategy. In the proposed control strategy, the controller makes use of an
estimator to forecast the plant output in order to supply the absent values (as a result of the delays or
vacant samples). To
determine the control signal that is effectively applied to the plant, in each sampling
interval, an estimator is used so as to compensate for the variations in the plant input signal.
Comment
:
(1)
We need a control technique that can
be applied on existing systems without high cost.
(2) Lower the assumption on the NCSs, such as network time delay is less than the sampling period.
(3) The estimator design could be studied. [3] use a model

based estimator, which based on ARMAX
model. Po
ssibly, the Kalman filter could be used.
3.3 controller design
Comment
C
onventional controller design technique does not apply. We can investigate some new method. Some
paper study the LQG/LQR controller design.
References
[1] Li

zhen Wu and Xiao

hong H
ao. A novel optimal controller design and evaluation for networked
control systems with time

variant delays. Presented at Measuring Technology and Mechatronics
Automation (ICMTMA), 2010 International Conference on.
[2] Guo

Ping Liu, Yuanqing Xia, David Re
es and Wenshan Hu. (2007, Design and stability criteria of
networked predictive control systems with random network delay in the feedback channel. Systems,
Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 37(2), pp. 173

184.
[3
] E. C. Martins and F. G. Jota. (2010, Design of networked control systems with explicit compensation
for time

delay variations. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE
Transactions on 40(3), pp. 308

318.
[4] L. Samaranayake,
M. Leksell and S. Alahakoon. Relating sampling period and control delay in
distributed control systems. Presented at Computer as a Tool, 2005. EUROCON 2005.the International
Conference on.
[5] R. A. Gupta and Mo

Yuen Chow. (2010, Networked control system
: Overview and research trends.
Industrial Electronics, IEEE Transactions on 57(7), pp. 2527

2535.
[6] Jin Wu, Fei

Qi Deng and Jing

Guang Gao. Modeling and stability of long random delay networked
control systems. Presented at Machine Learning and Cyberne
tics, 2005. Proceedings of 2005
International Conference on.
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