design of intelligent infusion system based on fuzzy...

Design of Intelligent Infusion System

based on Fuzzy Control

Yu Liu1, Benzhen Guo2, Jingjing Yang3, Zhihui Wang4 and Xiao Zhang5*

Hebei North University, Zhangjiakou, Hebei 075000, China

[email protected]

Abstract. Infusion is an important and widely used treatment method in present

medical practice. The dripping speed of infusion is an important parameter

during the infusion process. This paper introduced an intelligent infusion

system with dripping speed remote setting and abnormal situation alarm

function. The dripping speed control is achieved with fuzzy-PID control with

auto adjusted factors. The test results show the characteristics of fast response

and high accuracy of the intelligent infusion system.

Keywords: Intelligent infusion system; Fuzzy Control; PID Control.

1 Introduction

Clinical medicine has a rapid development in recent years, meanwhile, hospitals also

have strengthened the construction toward information, automation and intelligent.

The present status of the hospital in China is heavy workload on nurses, patients and

medical staff. Nurses adjust the infusion speed with experience and bare eyes, this

infusion speed management method can result in disastrous medical accidents

whether the infusion speed is too low or too fast. Fuzzy control refers to the

application of the fuzzy theory in the control technology. The fuzzy control uses

language variables instead of mathematical variables. The fuzzy control is suitable for

the industrial process without a mathematical model or hard to build a mathematical

model, variables in those process tend to be non-linear variables. Fuzzy control

doesn't need a specific mathematical model, it's an efficient way to solve uncertain

problems in non-leaner systems. The dripping speed control system in the intelligent

infusion system is a non-leaner system, therefore the fuzzy control method can be

used in the intelligent infusion system to achieve precise and accurate control of the

dripping speed.

*Corresponding Author

Advanced Science and Technology Letters Vol.141 (GST 2016), pp.204-208

http://dx.doi.org/10.14257/astl.2016.141.44

ISSN: 2287-1233 ASTL Copyright © 2016 SERSC

2 The Fuzzy Control Algorithm

Current fuzzy control algorithm tends to produce steady-state error, in order to

diminish the error to achieve the traumatic infusion dripping speed control, a adjusted

fuzzy control algorithm must be adopted.

2.1 Fuzzy - PID Compound Controller

To improve the precision and tracking the performance of the fuzzy controller, the

controller must use more variables, meanwhile, the number of rules and calculation

amount are also greatly increased. One way to solve this contradiction is using

different control method in different theory domain. Assume EP is one threshold,

when EEP; the system use fuzzy control; when E<EZ, the system use PID control.

Then, the fuzzy-PID control method not only improve the system's response time,

response accuracy and robustness, also the control method can realize high accuracy

fuzzy-PID control. The structure of the control method is shown in Fig.1.

Fig.1. The structure of the fuzzy-PID compound controller

The optimal switch point is selected based on the following idea: When the system

changes from fuzzy mode to PID mode, the optimal point is where the language value

equals to “zero” (ZE). The system is under PID control mode when E=ZE, the PID

algorithm is:

ninnpnn EKEEKUU )( 11 (1)

In (1), Kp is ratio coefficient, Ki is integral coefficient, U is the output control

variable of PID.

When language variable in fuzzy control equals to "zero" (ZE), the absolute error is

not necessarily zero, in fact, on this basis, the state performance of the system can be

improved according to the absolute error and error change trend. This integrator

works as integral function when the absolute error is increasing while the absolute

error is decreasing the integrator equals to a constant, when E=0 or the integral is

saturated, the integrator can reset automatically.

The simulation results show that the fuzzy-PID controller, comparing to the

conventional PID controller, advances in the anti-external disturbance, high

robustness, low overshoot and fine dynamic characteristics. Compare to the simple

fuzzy control mode, the fuzzy-PID control mode has good characteristics of accuracy

and precision.

Advanced Science and Technology Letters Vol.141 (GST 2016)

Copyright © 2016 SERSC 205

2.3 The PID Control Algorithm

PID controller is a kind of regulator based on proportional, integral, derivative

methods, it's the most widely used continuous system regulator. It has the

characteristics of simple structure and easy setting. Through the using experience and

theoretical analysis, the PID regulator can result in satisfied control effects on

different control objects. In the fuzzy-PID controller, the PID control algorithm is:

0

1 ( )( ) [ ( ) ( ) ]

( )p d

i

de tu t k e t e t dt T

T d t (2)

In (2), u(t) is the output of the regulator, e(t) is the deviation signal, e(t) equals to

the subtraction of the setting value and the output, kp is the proportional coefficient,

Ti is the time integral coefficient, Td is the time derivative coefficient.

The computer system is a kind of sampling control system, it can only can

calculate the control variable based on the sampling time deviation, thus to achieve

(2), the data must be discretized, and the differential equation in the continuous

system must be replaced by partial difference equation.

The discretization of the continuous system can use the equation:

( 0,1,2, , )t KT K n (3)

Integral with cumulative summation approximation:

0 00

( ) ( ) ( )k k

j j

e t dt e j T T e j

(4)

Differential of the first-order approximation:

( ) ( ) ( 1)de t e k e k

dt T

(5)

In (5), T is sampling period, e(k) is the deviation of number k sampling, e(k-1) is

the deviation of the number k-1 sampling. Based on (4) and (5), (3) is changed to:

0

( ) { ( ) ( ) [ ( ) ( 1)]}k

dP

Ji

TTu k K e k e j e k e k

T T

(6)

If the sampling period T is small enough, (6) is proximate simulation of the PID

algorithm. In real situation, every output is related to all the previous state, the

calculation isn’t efficient with (6), the deduction form of (6) is:

0 1 2( ) ( 1) ( ) ( 1) ( 2)u k u k a e k a e k a e k (7)

In (7), 0 1 2

2(1 ), (1 ),d d d

p p p

i

T T TTa k a k a k

T T T T .

Equation (7) is the PID control algorithm used in the intelligent infusion system.

2.4 Empirical Analysis of the Control Performance

To study the performance of the fuzzy controller with self-adjusting factors, the

mixed empirical analysis is used in this paper. On one side, under the same control


206 Copyright © 2016 SERSC

object, compare this controller to the fuzzy control fixed adjusting factors, on the

other side, by changing the parameters of the control objects, observe the robustness.

Selecting the typical second order link as the control object, the parameters of the

control objects and performance data of the two kinds of fuzzy controller is shown in

Table 1.

Table 1. Comparing of the performance of fixed adjusting and auto adjusting fuzzy controller

Parameters Fixed adjusting

fuzzy controller

Auto adjusting

fuzzy controller

T1 T2 ts 6p(%) ts 6p(%) 0.5 1 1.9 0 1.6 0

0.8 1.5 4.2 0 3.1 0

1 2 6.8 2.1 5.2 2.1

From the performance data, the auto adjusting fuzzy controller has low response

time and overshoot, and it also has better robustness on the changing of variables than

the fixed adjusting fuzzy controller.

3 The Implementation of the Fuzzy Controller

The control of the dripping speed uses fuzzy-PID compound control algorithm, this

algorithm can increase the system's precision as well as improve the system overall l

performance. The infusion speed deviation e and deviation rate ec are the input

variables of the fuzzy controller, the increment steps u of the stepper motor is the

output variable of the fuzzy controller, the control diagram is shown in Figure 2.

Fig. 2. The control diagram of the Fuzzy-PID controller


Copyright © 2016 SERSC 207

4 System Testing

The After the debugging of the system software and hardware, write the lower

computer software to the single chip's flash memory, connect the circuit, and test the

system in practical environment. The dripping speed is set by the mobile terminal and

after 30s the dripping speed is observed with stopwatch. The result is shown in the

table 2.

Table 2. The dripping speed test result

Base Dripping

Speed

Displayed

Dripping Speed

Observed

Interval(s)

Observed

Dripping Speed

44 drops/min 101 drops/min 30s 100 drops/min




The test results show a very satisfied response time from the set of dripping speed

to the destination dripping speed, and the results also show that the observed dripping

time deviated little from the displayed dripping time.

Acknowledgment. This project was supported partially by Population Health

Informatization in Hebei Province Engineering Technology Research Center, Medical

Informatics in Hebei Universities Application Technology Research and

Development Center, Zhangjiakou department of science and technology project

(1421012B and 1421054I).

References

1. Savran, A.: A multivariable predictive fuzzy PID control system. Applied Soft

Computing 13.5 (2013): 2658-2667.

2. Abidi, K., Xu, J.-X.: Discrete-Time Fuzzy PID Control. Advanced Discrete-Time Control.

Springer Singapore, (2015). 145-163.

3. Zhang, W.: The application of self-tuning fuzzy PID control method to recovering

AUV. Oceans, (2012). IEEE, 2012.

4. Agius, C.R.: Intelligent infusion technologies: Integration of a smart system to enhance

patient care. Journal of Infusion Nursing 35.6 (2012): 364-368.

5. Hayes, A.C.: Algorithm Sensor Augmented Bolus Estimator for Semi-Closed Loop

Infusion System. U.S. Patent Application 13/529,278.

6. Oruklu, M.: Infusion system which utilizes one or more sensors and additional information

to make an air determination regarding the infusion system. U.S. Patent Application

14/289,848.


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