2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
Application of Neural Networks inProcess Control: Automatic/Online Tuning ofPID Controller Gains for I B Vasu Murthy,
2
+ 10% Disturbance Rej ection
3 Y V Pavan Kumar, U V Ratna Kumari
13 , Department ofECE, lawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada.
2
Engineering Test Services, Aerospace Business Unit, Honeywell Technology Solutions Lab (Pvt) Ltd, Hyderabad. i
Abstract
-
2 3
[email protected],
[email protected] [email protected]
In the area of process control engineering, PID
controller plays a very vital role, which can be used as a process controller for the general industrial processes like liquid level, temperature, pressure, flow, and etc. Most of the research papers in the area of process control deals with setting up of these PID controller gains. There were many tuning algorithms evolved starting from the famous Ziegler - Nichols in 1942. But, still it is a major challenge for setting of PID controller gains.
Also, since PID controller is an off-line controller, the gains are
the raise/fall of the liquid to current of 4-20 rnA. And this
current is converted to 0-5 V using IIV converter and is given to process controller through data acquisition card. The control signal from the process controller is given to V/I converter to convert voltage to current. That current intern converted to pressure, to control the valve position. When liquid in the tank is at set point value and equal inlet and outlet flows, then both the control valves are closed.
set in offline and then the controller is put in to the process loop.
When outlet flow decreases, then liquid level increases there
Hence this conventional off-line PID controller cannot cope up
by float object rises. Then control valve will be closed and
non-linear
outlet valve opened. Hence, liquid level decreases. If liquid
disturbances/ noises. Hence PID controller is associated with
with
the
online
process
variations
due
to
the
level decreases to less than the set point value, then again
poor disturbance rejection. Based on these typical challenges from the conventional tuning methods, the paper proposes an original philosophy of providing ± 10% disturbance rejection
feature to the PID controller. The novelty used in the design is
float falls, then outlet valve will be closed and control valve opened. This process makes the liquid level to maintain a constant height.
automatic tuning ofPID controller gains by the use of intelligent predictors like Artificial Neural Networks (ANN). This provides the facility of online automatic tuning of PID controller gains and so, good disturbance rejection for the system. The whole system is modeled and simulated by using MATLAB/Simulink software. The results show that the system has good disturbance rejection up to ± 10% with the proposed control strategy. Keywords - Artificial Neural Networks (ANN), Intelligent - PID Controller, Liquid level system, MATLABISimulink, Open Loop Transient
Response
(OLTR),
PID
Controller,
PID
Tuning
methods
I.
INTRODUCTION
Level control is one of the most important aspects widely used in industrial process control. It got a wide range of applications like in nuclear power plants, chemical industries,
Fig.l Schematic diagram for liquid level control
food processing industries, water purification systems, and etc. As the parameters of the plant changes constantly, maintaining the liquid level at its set point value is difficult.
The transfer function of the liquid level control system is given in equation (1).
Hence, it is needed to design a controller which controls level
G(S)
of the liquid at its set point value and also it must accept the variable disturbances on the plant. PID controller [1] is the earliest used controller in the industries. But it is difficult to get
the efficient
control as
the PID gain
setting
is a
0.315 e-8.415S 12.826S + 1
(1)
II. MODELING OF THE LIQUID LEVEL CONTROL SYSTEM WITH CONVENTIONALPID CONTROLLER
challenging task. Literature survey has shown that many tuning methods have been proposed for optimal setting of
=
From
the literature review,
basically there
are
four
PID gains. But still they got some limitations. To overcome
approaches for tuning ofPID controller gains. They are given
all these limitations, this paper uses intelligent concepts like
as follows.
Artificial Neural Networks (ANN) to get the optimal PID gain parameters. Figure.1 [13] shows it schematic view of liquid level control system. Here Capacitance level transmitter converts
ISBN No. 978-1-4673-2048-1112/$31.00©20 12 IEEE
1.
Closed loop/Ultimate cycle methods
2.
Open loop transient response methods
3.
Closed loop peak overshoot/under shoot calculations
4.
Direct tuning from transfer functions
348
2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
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