Response of Flow Rate of Non-interacting Tanks Using NCS and Fuzzy Controller

Yogendra narayan1 Department of Electronics & Communication

Engineering, DCE Gurgaon,India

narayan.yogendra1986@gmail.com

Dr. Smriti Srivastava2

Department of Instrumentation & Control Engineering, NSIT New Delhi,India

smriti.nsit@gmail.com

Abstract - In This paper we discussed about Networked control systems (NCS) for controlling flow rate of two non-interacting tank processes using fuzzy logic controller. Basically NCS are used for network communication to enable exchange of information of plant response via communication channel. For controlling the flow rate of processes we use fuzzy logic controller with a network on a shared medium to connect plant & controller. The experimental application of NCS in this paper work is manufacturing plant monitoring to provide decision & command, factory automation Key words- NCS, FLC, MATI

I. INTRODUCTION The study of networked control systems combine the knowledge of computer networks and traditional control systems. The main objective of using communication networks for data transmissions in control systems are reduced system wiring, ease of system diagnosis as all information is available everywhere in the system, and increased system reliability. A network can be used to interconnect the components of a control loop. The industrial networks used in automation can guarantee certain reliability and time-delay bounds. This eases the design of controllers and stable operation can be determined with certainty. The information in the networks is transmitted as packets. Therefore as soon as we transmit a continuous-time variable through a network it is turned into discrete-time

A. NETWORKED CONTROL SYSTEM The feedback control systems [1], where the process sensors, actuators, and controllers are interconnected by a communication networks are called Networked Control Systems (NCSs). It is a type of distributed control systems. There are the advantages of using the network in terms of reliability, reduced wiring, reconfigurability and ease of system diagnosis as all the information is available[4][5] everywhere in the system. However implementing the communication

network induces the stochastic and time varying delay which can degrade the performance of the system and even could make the system unstable. Moreover the time delays are the function of device processing times and communication rate. Research in NCSs is different from that in conventional different from that in conventional time-delay systems where time delays are assumed to be constant or bounded. Because of the variability of network-induced time delays, the NCSs may be time-varying systems which make analysis and design more difficult.

B. INTELLIGENT CONTROL Conventional control engineering approaches [14] are based on mathematical models, typically using differential and difference equations. However, these methods can only be applied to a relatively narrow class of models, including linear models and some specific types of nonlinear models [2]. Application of classical control design falls short in the situation when no mathematical model of the process to be controlled is available, or when it is nonlinear to such a degree that the available techniques cannot be applied.

C. VARYING TIME-DELAY SYSTEMS One major challenge for NCS design [9] is the network induced delay effect in the control Loop. Some delays, i.e the transmission time delay that it takes for a transmitter to send out data, are constant. Others including sequencing time caused by the waiting consequence of medium access are naturally time-varying and sometimes hard to estimate .A simple approach is to examine the longest time delay that can be tolerated if the controller is given.For instance, one simple method is to analyze the maximum allowable frequency-domain shift of the systems' Eigen values caused by time delay. Similarly in time-domain, the Maximum Allowable Transfer Interval (MATI) was proposed in [6]-[7] to examine the maximum allowable time delay for linear NCS. However, this usually leads to time-

consuming search-test procedure. Network caused delay directly contributes to the delay in the control loop and contributes towards instability of the system.

Fig.1 delay caused by the network

Controller:-It used for controlling the response of plant with delay produced by network. Delay :-To avoiding the conjunction of data during transmission. Process :-Two non-interacting tank system connected in cascading manner Reference :-It is set point

II. MODELLING OF SECOND ORDER PROCESSES

A. INTRODUCTION A second order system is one whose output, y(t), is described by the solution of a second order differential equation .For example the following equation describes a second order linear system Consider a two series connected tank of height h1&h2 as shown in figure

Fig.2 two cascade first order tank

A second order system is one whose output, y(t), is described by the solution of a second order differential equation .For example the following equation describes a second order linear system

)(012

2

2 tbfyAdtdyA

dtydA =++ 1)

If A0≠0 then this equation yields

)(22

22 tfky

dtdy

dtyd

p=++ τξτ (2)

where 0

22

AA

=τ ,0

12AA

=τξ and 0A

bk p = . Equation

(2) is in the standard form of a second order system. Where = is the natural period of oscillation of the system ξ = is damping factor

pk = is steady-state or static or simply gain of the system

If equation (2) is in terms of derivation variables, the initial condition is Zero and its Laplace transformation yields the following standard transfer function of a second order system.

12)()()(

22 ++==

ss

kSFSYSG p

ξττ (3)

III. NONINTERACTING CAPACITIES When a system is composed of two noninteracting capacities ,it is described by a set of two differential equations of the general form:

)(11, tfky

dtdy

p=+τ ..first capacity (4a)

)(22,, tfky

dtdy

p=+τ ..second capacity (4b)

In other world ,the first system affect the second by its output, but converse is not true .the corresponding transfer function are

1)()(

)(,

1

1

11

+==

s

kSFSY

SG p

τ (5)

1)()(

)(,,

2

2

22

+==

s

kSFSY

SG p

τ (6)

The overall transfer function between the external input )(tFi and )(2 tF is

1*

1)(*)(

)()(

*)()(

)(,,

2,

121

2

2

1

1

++===

s

k

s

kSGSG

SFSY

SFSY

SG pp

ττ (7)

12)()()(

22 ++==

ss

kSFSYSG p

ξττ (8)

IV. FUZZY TABLE

Where ,NB :-negative big, NM:- negative medium ,NS:- negative small ,Z:- zero, e:-error PS:- Positive small, PM:- Positive medium ,PB:- Positive Big, de:-change in error

V. SIMULATION AND RESULT In this section ,the result of a fuzzy controlled second order processes (tank system) is compared without networked control system to fuzzy controlled processes with network control system considering plant and controller data losses and delay due to sampling period. Because in every practical control loop there is a time-delay resulting from sampling, computations of the control signal and the limited speed of the measurement sensors.

Fig 4.Communication network block diagram

Fig .5 plant with fuzzy controller

Fig. 5. Response of plant the above figure shows, the response of plant by a delay of 1 sec,with 35 % plant data packet losses and delay of controller 1 sec with 5% controller data packet losses , with a sampling period0.01sec in both case plant data and controller data packet.

Fig.5 Number of dropped packet from plant data

Fig 6 Number of packet drop by network

The above figure is showing the response of number of packet dropped during controlling the system to other place via communication Network .Its explain the how data-packet losses in communication of two systems

Fig.7 No of packet time out during communication

between plant & controller

The below figure shows the time out for packet transmitting

de/e NB NM NS Z PS PM PB

NB NB NB NB NM NM NS ZE NM NB NB NM NS NS ZE PS NS NB NM NS NS ZE PS PM Z NM NS NS ZE PS PS PM PS NM NS ZE PS PS PM PB PM NS ZE PS PS PM PB PB

PB ZE PS PM PM PB PB PB

Fig.8 Ratio of losses during communication between plant & controller

Fig.9 Number of data packet in buffer queue during controlling plant response.

Fig. 10 Reception buffer data of plant receiver

VI. CONCLUSIONS AND FUTURE SCOPE In this paper we conclude that the great importance of NCS is to control the plant response via communication network from one place to another place ,here the response of plant can be controlled with the help of Networked Control System and fuzzy Logic Controller. From results it is clear that when 35% plant signal data & 5% controller signal losses due to communication channel data losses with a sampling period of 0.001 sec , a delay of 1 second is produced in response .If plant data losses is 90% with a sampling period 0.5 second then a delay of approximate of 5.4second is produced in response .If both plant and controller data losses is 90% with a sampling period of 0.5 second then the delay is 5.4 second .It means controller signal has more importance as compared to plant signal for un-delayed response with high sampling rate.

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