**5.1. Numerical simulations**

In this section numerical simulations using the Network scheduling strategy based on Frequency Transmission are exposed. The network scheduling strategy dynamically adjusts the frequencies, considering the participation of several nodes of the NCS. These scheduling approach shows a way to manage the network resources, especially with a limited network bandwidth. These techniques avoid network delays during transmission. Numerical simulations shows that the dynamical changes of this strategy improve the RTDS response under fault scenarios.

0 5 10 15 20 25 30

Pitch Signal

Issues on Communication Network Control System Based Upon Scheduling Strategy Using Numerical Simulations

63

Time

Yaw Signal

<sup>0</sup> <sup>5</sup> <sup>10</sup> <sup>15</sup> <sup>20</sup> <sup>25</sup> <sup>30</sup> -15

0 5 10 15 20 25

<sup>0</sup> <sup>5</sup> <sup>10</sup> <sup>15</sup> <sup>20</sup> <sup>25</sup> -15

Time

Yaw Signal

Time

control scheme, new frequencies are assigned to the sensor nodes in which the task period is changed therefore the effect of fault scenario is minimized balancing the amount of data sent

Numerical simulations were performed using the values of the maximum, minimum, and real frequencies besides the computational time, the values used for the sensor nodes are shown

Figure 9 shows the frequencies controlled, this control bounds the frequency into a

Figure 10 shows system behavior during 30 seconds using frequency transition model. Nominal frequencies start in schedulability region during 8 secs, system transmits with this

Time

Pitch Signal



**Figure 8.** Pitch and yaw signals in a fault scenario

schedulability region where the IAE is low.

Degree

over the network.

in the table 1.

Degree

**Figure 7.** Pitch and yaw signals in a fault-free scenario

Degree

Degree

The relationship amongst IAE and sampling periods of the primary sensing tasks is shown in Fig. (6). When the network transmits data without overloading or under sampling the output signals of pitch and yaw angles are similar to induced reference signal, so that the value of IAE is small. In contrast, when the transmission rate of sensor task set exceeds the upper bound data transmission rate the system becomes unstable, IAE increases accordingly.

**Figure 6.** Values of the IAE using different sampling periods

The sensing periods was fixed in 10 miliseconds, sampling during the time simulation with this rate. In a fault-free scenario the pitch and yaw singnals track the reference signal, this behaviour is presented in Fig. 7

It is assumed that in a fault scenario the change in periods of tasks may be produced, this induces a network utilization that possibily can not be supported by the bandwidth or perhaps stops data tranmission continuity. The effect of a fault over the RTDS is shown in the Fig. 8. In this case the periods of sensor tasks produce a low sampling resulting in loss of control.

For the particular case study was made off-line analysis of the frequencies with which can be transmitted without increasing the value of the IAE for the sensing tasks. These ranges serve as parameters for the calculation of new transmission frequencies using the proposed model.

When fault occurs a signal is transmitted to the scheduler, this signal contains the *id* of sensor with fail and the parameters of the tasks on execution before the failure. The scheduler uses the frequency range and time of execution of the sensor tasks that has just entered, scheduler knows frequencies ranges of the tasks that continue without fail. This information feeds frequency transition model to compute a new frequency transmission range through a LQR 62 Numerical Simulation – From Theory to Industry Issues on Communication Network Control System Based Upon Scheduling Strategy Using Numerical Simulations <sup>15</sup> 63 Issues on Communication Network Control System Based Upon Scheduling Strategy Using Numerical Simulations

**Figure 7.** Pitch and yaw signals in a fault-free scenario

14 Will-be-set-by-IN-TECH

In this section numerical simulations using the Network scheduling strategy based on Frequency Transmission are exposed. The network scheduling strategy dynamically adjusts the frequencies, considering the participation of several nodes of the NCS. These scheduling approach shows a way to manage the network resources, especially with a limited network bandwidth. These techniques avoid network delays during transmission. Numerical simulations shows that the dynamical changes of this strategy improve the RTDS response

The relationship amongst IAE and sampling periods of the primary sensing tasks is shown in Fig. (6). When the network transmits data without overloading or under sampling the output signals of pitch and yaw angles are similar to induced reference signal, so that the value of IAE is small. In contrast, when the transmission rate of sensor task set exceeds the upper bound

IAE

The sensing periods was fixed in 10 miliseconds, sampling during the time simulation with this rate. In a fault-free scenario the pitch and yaw singnals track the reference signal, this

It is assumed that in a fault scenario the change in periods of tasks may be produced, this induces a network utilization that possibily can not be supported by the bandwidth or perhaps stops data tranmission continuity. The effect of a fault over the RTDS is shown in the Fig. 8. In this case the periods of sensor tasks produce a low sampling resulting in loss of control. For the particular case study was made off-line analysis of the frequencies with which can be transmitted without increasing the value of the IAE for the sensing tasks. These ranges serve as parameters for the calculation of new transmission frequencies using the proposed model. When fault occurs a signal is transmitted to the scheduler, this signal contains the *id* of sensor with fail and the parameters of the tasks on execution before the failure. The scheduler uses the frequency range and time of execution of the sensor tasks that has just entered, scheduler knows frequencies ranges of the tasks that continue without fail. This information feeds frequency transition model to compute a new frequency transmission range through a LQR

<sup>5</sup> <sup>10</sup> <sup>15</sup> <sup>20</sup> <sup>65</sup>

Period (milisecs.)

Yaw Signal

data transmission rate the system becomes unstable, IAE increases accordingly.

<sup>5</sup> <sup>10</sup> <sup>15</sup> <sup>20</sup> <sup>50</sup>

**Figure 6.** Values of the IAE using different sampling periods

Period (milisecs.)

Pith Signal

**5.1. Numerical simulations**

under fault scenarios.

behaviour is presented in Fig. 7

IAE

**Figure 8.** Pitch and yaw signals in a fault scenario

control scheme, new frequencies are assigned to the sensor nodes in which the task period is changed therefore the effect of fault scenario is minimized balancing the amount of data sent over the network.

Numerical simulations were performed using the values of the maximum, minimum, and real frequencies besides the computational time, the values used for the sensor nodes are shown in the table 1.

Figure 9 shows the frequencies controlled, this control bounds the frequency into a schedulability region where the IAE is low.

Figure 10 shows system behavior during 30 seconds using frequency transition model. Nominal frequencies start in schedulability region during 8 secs, system transmits with this

#### 16 Will-be-set-by-IN-TECH 64 Numerical Simulation – From Theory to Industry Issues on Communication Network Control System Based Upon Scheduling Strategy Using Numerical Simulations <sup>17</sup>


Figure 11 shows the computer network as well

Sensor 4

Sensor 3

Sensor 2

Sensor1

Controler

**6. Conclusions**

**Author details**

**7. References**

*Faults*, Nancy, France.

0 1 2 Time(secs.)

65

Issues on Communication Network Control System Based Upon Scheduling Strategy Using Numerical Simulations

**Figure 11.** Sensing activiity and the related use of the computer networking using the Truetime toolbox

This work show a study of network scheduling strategy using numerical simulations. A simulation of a particular RTDS, a 2-DOF helicopter, is built using TrueTime as real-time simulation tool. A network scheduling strategy based on changes of frequency transmission rates is implemented in order to expose the advantages of using dynamic scheduling in an ad-hoc implementation for the network of a NCS. The use of numerical simulations aid to

[1] Cervin, A., Henriksson, D., Lincoln, B., Eker, J. & Årzen, K.-E. [2003]. How does control timing affect performance? analysis and simulation of timing using jitterbug

[2] Cervin, A., Ohlin, M. & Henriksson, D. [2007]. Simulation of networked control systems using truetime, *Proc. 3rd International Workshop on Networked Control Systems: Tolerant to*

[3] Chen, H., Luo, W., Wang, W. & Xiang, J. [2011]. A novel real-time fault-tolerant scheduling algorithm based on distributed control systems, *Computer Science and Service*

explore several considerations in design and analysis of a NCS.

and truetime, *Control Systems, IEEE* 23(3): 16 – 30.

*System (CSSS), 2011 International Conference on*, pp. 80 –83.

*Universidad Nacional Autónoma de México, México*

Oscar Esquivel-Flores, Héctor Benítez-Pérez and Jorge Ortega-Arjona

**Table 1.** Maximum, minimum, and real frequencies besides the computational time

**Figure 9.** Frequencies bounded into a schedulability region usign a frequency transmission controller

rate. In second 9 fault appears with a change of context, system transmits with this frequency until second 18. In second 19 frequency transition model change frequencies of backup tasks.

**Figure 10.** Pitch and yaw signals in a fault scenario using frequency transmission model

Figure 11 shows the computer network as well

**Figure 11.** Sensing activiity and the related use of the computer networking using the Truetime toolbox
