UWE Bristol Engineering showcase 2015

Zongyuan Tao
Electrical and Electronic (Beng)
Project Supervisor
Professor Q. M. Zhu
Neural Networks Based Control System design for a continuously stirred
tank reactor
Introduction
In modern industry, continuously
stirred tank reactor (CSTR) is widely
used to produce polymer due to its low
cost, highly efficient ability of heat
exchange and stability of product
quality . In general, CSTRs are
controlled to operate around a certain
equilibrium point linked to the optimal
output or optimal productivity of a
process to pursue a high conversion
rate and maximize economic
benefits.Therefore, designing a proper
control system for CSTR is really
significant. As a control target, CSTRs
are highly non-linear, dynamic and
influenced by many unfavourable
factors. Therefore designers cannot
use those traditional control methods
for linear system to solve CSTR easily.
The neural network based control
system has the superiority that it has
the high adaptability and the ability of
self-learning. These features give
neural network control the ability to
solve the nonlinear situations.
Therefore, choosing neural network
based control is an effective scheme to
solve those problems in CSTR system.
A common type CSTR
Design steps of a neural network based
NARMA-L2 controller
Step1 Construct the neural network: Set
the number of neurons in hidden layer,
the sampling time and the total input
number of the controller include delayed
plant input and delayed time output.
Step2 Generate training data: a large
number of random step inputs are
created and then acted on the plant
model. These inputs and the
corresponding outputs will be recorded
and collected as a data set by MATLAB.
Step3 Neural network training: the neural
network is trained by using the generated
training data. The BP (Back
Propagation)algorithm is used to adjust the
weights within each neurons.
A neural network based NARMA-L2 control system
Simulation results
It can be noted that NARMA-L2 neural control has the lowest
maximum overshoot
(peak value 481.5 K)
and shortest
regulating time (less
than 1/3 of the other
two’s) within these
three methods. It
indicates that NARMA-
L2 neural control has
superiority in stability
and rapidity.
Comparison with other two types of controllers
Step 1
Step 2
Step 3
Influence of the network structure
The performance of 3 NARMA-L2 controller
can be shown by the waveforms below. It
can be noted that the controller with 13
neurons in hidden layer is better than the
other two controllers which have 9 and 17
neurons respectively in hidden layer. The
best number of neurons in hidden layer may
depends on the complexity of the model.
Temperature
Controller output
Project summary
The aim of this study is to design a proper neural
network based control system for a CSTR and to make
initial computational experimental demonstrations
based on MATLAB.
Project Objectives
• Take critical survey on the relevant researches.
• Choose a sensible model to theoretically guide the
design of control system, build it properly by using
MATLAB Simulink.
• Research and learn the theory of artificial neural
network and neural network based control in
order to select a proper method within variety of
neural network based CSTR control methods. The
neural network based NARMA-L2 control is chosen
in this project.
• Design the neural network based NARMA-L2
controller using MATLAB Simulink and then test
and modify the design in controlling the model,
sort out the simulation results.
• Analysis the simulation results, comparing the
control performance with the other control
methods.
Project Conclusion
In this project, a neural network base NARMA-L2
controller is designed and well controlling the CSTR
model based on MATLAB. The model which
appropriately reflects the non-linearity of CSTR is
built based on Simulink. It indicates that neural
network based NARMA-L2 controller has the ability
to solve non-linear systems. By comparing the control
performance with the traditional PID control and selfturning
PID control, the high stability and rapidity of
network based NARMA-L2 controller have been
reflected. Furthermore, the influence of the structure
of neural network within NARMA-L2 neural controller
is discussed.