Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 63–66___________________________________________________________________________________________________________Simulation of Main Steam Temperature Control System Based on Neural NetworkFengzhi Dai, Yujie Yan, Baochang Wei, Yuxing Ouyang, Lingran An

College of Electronic Information and Automation,
Tianjin University of Science and Technology, China;
E-mail: daifz@tust.edu.cn
www.tust.edu.cn

Abstract

This paper designs a PID control system based on the BP neural network. The control system can adjust
three adjustable parameters of PID controller through BP neural network algorithm. The main steam
temperature control system of the thermal power plant was taken as the object, and simulation analysis
was performed through MATLAB. The experimental results show that the design scheme has good
performance and anti-interference ability, and has certain practicability to the main steam temperature
control system of thermal power plant.
Keywords: BP neural network, PID control, MATLAB, temperature

1. Introduction

control system, it also increases the test of anti-jamming
capability. It is another practical attempt and exploration
to combine the traditional PID control and neural network
to control to achieve the main steam temperature control4.

Control theory has undergone several stages ofdevelopment and made historic contributions toaerospace, military and industry. However, both theclassical control theory and the modern control theory
rely too much on the mathematical model, which limits
these applications greatly1.
Main plant temperature control system is a typicallarge delay, nonlinear and time-varying system2. If weuse a conventional PID controller, we will not achieve thedesired control effect. The work of this paper is to selectthe most widely used BP neural network algorithm and tocontrol the main steam temperature of the power plant bycombining the control strategy of three-layer BP neuralnetwork with PID classic incremental numerical control
algorithm. Because the main steam temperature of thepower plant is a time-varying uncertain object, it has
different mathematical models under different load values.
Basing on the data of the literature3, we conductedsimulations under different loads. In addition to verifying
the following performance of the neural network PID

2. PID temperature control system based on BP
neural network
BP neural network is a kind of multi-layer feed-forward
network. It follows the error backwards and can learn a
lot of input and output information. To minimize the errorof network, it uses the steepest descent learning method
to adjust the threshold and weight values by the backpropagation.
2-1. PID control system based on neural network
In the actual control system, the PID controller still
occupy more than 90%. In this paper, the advantages of
the classical PID controller and neural network will beshown. As long as the input-output error of the system is
not 0, the system continuously adjusts the neural network
weights to obtained optimal PID controller parameters.

Copyright © 2018, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 63–66___________________________________________________________________________________________________________Use gradient descent method to correct neural network
weight coefficient:

PID control system based on neural network is shown inFig.1.

wli3  k   2-2. Algorithm of PID control system based on BPneural networkwli3  k    l3  i2  k   wli3  k  1The expression of the incremental PID control algorithmis:wli2  k    i2  j1 k   wli2  k  1

(1)

Because the kp , ki , kd are adjustable coefficients, theabove formula can be expressed as:x1
1  tanhx   x e  x
2e e

Summarize the algorithm steps are as follows:
Determine the structure of BP neural network, that is,determine the number of input layer, output layer andhidden layer node. Give the initial value and thelearning efficiency of each layer's weight coefficient.
 Give the input rin(k) and output yout(k) of thesystem and calculate the error E(k).
 Calculate the input and output of neurons in eachlayer of neural network, and the output of output
layer is the three adjustable parameters of PIDcontroller.
 Calculate the PID controller output u (k).
 Learning through neural networks, online adjustmentof the weighted coefficient, to achieve adaptive
control of PID parameters.
 Set k = k + 1, return to the first step.

(2)

(3)

The activation function of hidden layer neurons is:f  x   tanh  x  e x  e xe x  ex

3. Application of neural PID control system formain steam temperature system in power plant(4)

Thermal power plant power supply system by the boiler,
steam turbine generator and other equipment. There aremany factors that determine the stability and efficiency of
the thermal power plant, and the main steam temperatureof the boiler is the most important factor that affects theSelect the performance index function E(k), our goal isto minimize E (k):E k  1rink   yout k 22(8)Among them, f(*) is a nonlinear function trained byneural network. This design chooses 4-5-3 BP neuralnetwork structure. Because the three coefficients must be
positive, the activation function of the neurons in theoutput layer is a non-negative5. The formula is as follows:g x  (7)The same can be introduced hidden layer weight value
formula:uk   uk  1  k p ek   ek  1  ki ek u ( k )  f [ u ( k  1), k p , k i , k d , e ( k ), e ( k  1),e ( k  2 )](6)

In the formula, α is the inertia coefficient and η is thelearning rate. The above formula adds the inertia term tomake the system converge quickly. Through calculationand simplification, get the output layer weight calculation
formula3:

Fig.1 PID control system based on neural network k d ek   2ek  1  ek  2E k 3 
3   wli k  1
wli

(5)

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Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 63–66___________________________________________________________________________________________________________stability and accuracy of the power plant. And the main
steam temperature control system commonly used in
power plants is a PID cascade control system.
Neural network-based power plant main steam
temperature control scheme, is to combine the neuralnetwork with the traditional PID controller to make up
for the artificial tuning PID parameters inaccuracy. The
block diagram of the main steam temperature control
system of power plant controlled by neural network isshown in Fig.2.

1.4

1.2

rin,yout

1

0.8

0.6

0.4

0.2

00200

400

600

800

1000 1200time(s)1400

160018002000

Fig.3 tracking curve based on classical PID control system

The PID control system based on BP neural network of
the 100% load value is simulated, and the experimental
results are shown in Fig.4 and Fig.5.

Fig.2 Power plant main steam temperature control system

The main steam temperature control system has manyfeatures. Such as the fast response, high precision, moreinterference6. It is usually required that the main steam
temperature be controlled as accurately as possible rather
than within a certain safety margin. In fact, there is asaying that about every 5 degrees lower thermal
efficiency decreased by 1%. Under different loads, thesystem has different transfer function. The formula isshown in the table1.

Fig.4 Tracking curve of PID based on BP neural network

Table1 The formula table
LoadValue

37%

50%

75%

100%
Fig.5 kp, ki, kd Parameters curve

TransferFunction

5.07(1  28s ) 2

3.067(1  25s ) 2

1.657(1  20s) 2

0.815(1  18s ) 2

By comparing the two tracking curve, we can get theresult that the PID controller based on NN has goodperformance, small overshoot, and short adjustment time.
And we can perfectly determine the three adjustableparameters of the PID to achieve the best control effect.

4. Simulation of main steam temperature control
system based on neural network.
4-1. Simulation Analysis of Different Control Systems
under the Same Load

4-2. Simulation analysis of PID control system based on
BP neural network under different loads

The classic PID control system of the 100% load value issimulated. The experimental results are shown in Fig.3.

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Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 63–66___________________________________________________________________________________________________________5. Conclusion

The experimental simulation analysis of different loads
under PID simulation combined with neural networks isanalyzed. The experimental results are shown in Fig.6.
The left figure shows the tracking curve at 37% load andthe right figure shows the tracking curve at 100%.

At present, the neural network PID control system iswidely used in industry, agriculture and other fields. This
control system also controls the drying of the barley malt,
the raw material for beer. In addition, it can also be usedin car medical thermostat control system. After wecontinue to explore, in the future neural network control
must have a greater application prospects.
Acknowledgements
The research is partly supported by the Research Fund for
the Reform in Education from Tianjin Municipal
Education Commission of China (171005704B). It is also
supported by the Science & Technology Fund Planning
Project of Tianjin Higher Schools (20120831,20140710-1400020005). The design of the system is also
supported by Tianjin Wiseman Optical Instrument CO.,LTD.

Fig.6 Tracking curve of PID based on BP neural network

Under different loads, simulation results show that thesystem is good. Especially when the system is running atfull capacity, the best control effect can be achieved
perfectly.
4-3. Simulation analysis of the interference immunityof the control system

References
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3. Han Li, Zhang Zhen-yu. The application of immune
genetic algorithm in main steam temperature of PID
control of BP network. Physics Procedia, 24: 80-86, 2012.
4. Wei Jiang, Xuchu Jiang. Design of an Intelligent
Temperature Control System Based on the Fuzzy
Self-Tuning PID. Procedia Engineering, 43:307-311,
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5. S. Rominus Valsalam, Anish S, B. Raja Singh. Boiler
Modelling and Optimal Control of Steam Temperature inPower Plants. IFAC Proceedings, 42(9):125-130, 2009.
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Due to the interference of the main steam temperature
control system in thermal power plants, theanti-interference ability of the system should also be
improved in practical application. In the simulation can
add a 0.1 interference to simulate the actual power plantinterference signal. The experimental results are shown inFig.7.

Fig.7 Step response curve plus interference

According to the simulation diagram, although there isfluctuation in the interference, the system quickly returns
to its normal state. This shows that the control system hasa good anti-jamming capability.

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