1. The emergence to exchange
information through a Wireless
communication channels in a network
control system (NCS) implies time
varying delays in the control loop that
can affect the performance of closed
loop systems and drive it to instability.
An Articulated Heavy Vehicle (AHV)
with Active Trailer Steering (ATS)
system considered.
AHV was modeled with three degree-
of-freedom in yaw plane. A NCS
based on LQR-output feedback
designed to ensure that the poles of
the closed-loop maintain prescribed
stability margin despite the variability
of the network delays. Therefore, a
gain scheduler controller is developed
to select appropriate LQR gain for
selected time-delay.
The considered motions in 3 DOF
car-trailer model are the car lateral
speed V, yaw rate r. and the
articulation angle between the car
and trailer ψ, respectively.
Nominal linear state-space is
obtained with matrices 𝑨𝑨, 𝑩𝑩 and 𝑪𝑪.All
matrices are consist of nominal
physical parameters based on
modified equations of motion for
ATSC case
C(s)
Controller
Server
G(s)
u(t)
U(t-T)
y(t)
y(t)
Client Side Server Side
1/S
A
CB
u(t) Bu(t) x(t) y(t)
In order to evaluate the car-trailer system’s
lateral stability, the linear quadratic (LQR_
technique is applied to the design
controller.
In a standard Client-Server WiNCS
architecture, the control command 𝑢𝑢 𝑡𝑡 =
𝐾𝐾 𝑥𝑥(𝑡𝑡) is computed by client and
transmitted to the server through wireless
sensor networks. The server receives the
data packets with some delays and
transfer it to the plant to sample the plant’s
output 𝑦𝑦 𝑡𝑡 . Therefore, the received output
by the client includes certain delays.
Using Laplace transform, we implemented
the transfer function of this system to
construct internal dynamic delays in
MATLAB.
𝐻𝐻 𝑠𝑠 =
𝑌𝑌(𝑠𝑠)
𝑅𝑅(𝑠𝑠)
= 𝑪𝑪 𝑆𝑆𝑰𝑰 − 𝑨𝑨 − 𝑩𝑩𝑩𝑩 𝑒𝑒−𝑠𝑠𝑠𝑠 −1
𝑩𝑩 𝑅𝑅 𝑆𝑆
u(t) y(t)
x’(t) = Ax(t) + B u(t)
y(t) = C x(t)
K
r(t)
(a) (b)
Figure 1: Articulation angle versus time for the case ATSC
(a) without controller, (b) with controller.
u(t) y(t)
x’(t) = Ax(t) + B u(t)
y(t) = C x(t)
K
r(t)
𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗
Figure
2:Articulatio
n angle
versus time
for with
internal
delay from
1s to 10 s.
Abstract Controller Design for ATSC Case Gain Scheduling Controller
Vehicle Modeling
Problem Statements
Adding certain time-delays to the system,
affects the performance of the closed-
loop system and even drives it to
instability. Based on LQR-output
feedback controller, an extended
controller is designed to ensure poles of
the closed loop system is maintains
within prescribed stability margin despite
the variability of network delays.
As the initial step, the stability of
controller with internal delay is computed
by the developed gain scheduler using
e𝑖𝑖 𝑖𝑖 𝑨𝑨 − 𝑩𝑩𝑩𝑩 ) < 0. If such condition is
not satisfied, then a new transfer function
has to be developed such that a delay of
t sampling period is replaced by t poles
in the original transfer function with no
delay. Next, a new control gain matrix
𝑲𝑲𝒅𝒅 , will be computed for this new
developed system based on LQR
techniques.
[1] Nikolakopoulos, G.; Panousopoulou, A.; Tzes, A.; Lygeros, J., "Multi-
hopping Induced Gain Scheduling for Wireless Networked Controlled
Systems," Decision and Control, 2005 and 2005 European Control
Conference. CDC-ECC '05. 44th IEEE Conference on , vol., no.,
pp.470,475, 12-15 Dec. 2005
[2] Shamim, R., Islam, M., and He, Y., "A Comparative Study of Active
Control Strategies for Improving Lateral Stability of Car-Trailer Systems,"
SAE Technical Paper 2011-01-0959, 2011, doi:10.4271/2011-01-0959.
u(t) y(t)
x’(t) = Ax(t) + B u(t)
y(t) = C x(t)
K
r(t)
𝑒𝑒−𝑗𝑗𝑗𝑗𝑗𝑗
𝑲𝑲𝒅𝒅
(a) (b)
Figure 3: Articulation angle versus time for the case ATSC with
T=1s to 1000s (a) without control gain, (b) with control gain
Figure
4:Articulation
angle versus
frequencies for
the case ATSC
with T =1s to 200s
after stabilization
References
Abstract
UML Wireless Sensor Networks
(WSNs) systems are deployed to
monitor specific phenomena. The
design of WSNs is prone to errors and
debugging and is very challenging due
to the complex interactions of software
components in a sensor node.
Moreover, WSNs systems have limited
power sources which lead to the
necessity of minimizing power
consumption utilization during the
design. This poster presents a set of
software patterns that can be used as a
basis for software design of a WSN.
The UML is used to capture the
hardware and the software components
of a WSN system and this in turn is
used for power consumption analysis of
the WSN during the early stages of the
development cycle.
UML Modeling and Power Consumption Analysis Framework for WSN
Animated UML Diagrams Sequence Diagram Comparison
 Swim lanes are used to
capture the event handlers.
 Events are triggered through:
 Timers.
 Components UML
Classes: Routing Class,
Radio Class, and MDA
Class.
 Sequence Diagram
Comparison Feature:
 System Statues
 Signal Paths
 Time Components
 Event Parameters
Model &Simulator Integration
Log File For Node 1
Cycle CPU RED Yellow Green Radio Board Flash Total
2239391893 0.0033433 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0294452
2240855324 0.0033433 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0294452
2240855325 0.0075667 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0336687
2240855649 0.0075667 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0336687
2240855650 0.0033433 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0294452
..
..
..
2244851990 0.0075667 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0336687
2244853876 0.0075667 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0336687
2244853877 0.0033433 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0294452
2246456924 0.0033433 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0294452
2246456925 0.0075667 0.0022 0.0022 0.0022 0.0188 7.0E-4 2.0E-6 0.0336687
Debugging Statements Trace file
1 22406482 |Timer0Handler| St
1 22406624 |CallSend| St
1 22408839 |Timer0Handler| En
1 22449918 |CallSend| En
1 22450083 |SendDoneHandler| St
1 22450218 |CallTimer0| St
1 22450521 |SendDoneHandler| En
 Code Generation inserts
debugging statements.
 Parsing tools analyzes the
log files to feedback the
results to the model.