19-05-2012, 11:04 AM
Analysis of Fuzzy PID and Immune PID
Controller for Three Tank Liquid LevelControl
Analysis of Fuzzy PID.pdf (Size: 361.46 KB / Downloads: 81)
Abstract
In industrial control systems the liquid level is
carrying its significance as the control action for level control in
tanks containing different chemicals or mixtures is essential for
further control linking set points. The three level control models
are considered in our work. The conventional control algorithm is
difficult to reach required control quality with more strict
restriction on overshoot. Design a parameter self-tuning
PID-controller based on fuzzy control, which can adjust
PID-parameters according to error and change in error.
Biological immune system is a control system that has strong
robusticity and self-adaptability in complex disturbance and
indeterminacy environments. The artificial intelligence technique
of fuzzy logic and immune controller is adopted for more reliable
and precise control action which incorporate the uncertain factors
also. In this work the comparison of the conventional model, fuzzy
model and immune feedback mechanism is clarified.
I. INTRODUCTION
Artificial Immune System (AIS) is a new branch of
computational intelligence inspired by the biological aspects
of the immune system. Immunology as a study of the immune
system inspired the evolution of artificial immune system,
which is an area of research over the last few years. Artificial
immune system imitates the natural immune system that has
sophisticated methodologies and capabilities to build
computational algorithms that solves engineering problems
efficiently. They are useful in constructing novel computer
algorithms to solve complex engineering problems. At the
present time, the immune system has already had the certain
research results in the projects, but is still needed to be
researched on its function and several mechanisms. The
human immune system has motivated scientists and engineers
for finding powerful information processing algorithms that
has solved complex engineering tasks. In cell level,
immunology concerns mainly in T lymphocytes and B
lymphocytes, focusing primarily on their interactions.
Modern immunology shows that there are different cells with
various functions in immune system, for instance, antibodies
attach on the B lymphocyte surface. Each antibody has
idiotope which acts as antigen, paratope which recognizes
other antibodies and antigens, and epitope which can be
recognized by other antibodies and antigens. There are
different immune system models that explore various immune
mechanisms and cell roles in the immunity process; amongst
those is idiotypic immune network theory. It is interesting to
observe that the process of recognition, identification and
post processing involve several mechanisms such as the
pattern recognition, learning, communication, adaptation, self
organization, memory and distributed control by which the
body attains immunity.
MATHEMATICAL MODELING OF THREE TANK SYSTEM
In this paper, the liquid level control system of a container
water tank system is discussed. A 3 – container water tank is
usually connected by three first-order non periodic inertia
links in series, and its structure can be schematically shown in
Fig.1.
III. THE SELF TUNING CONTROL PRINCIPLE OF FUZZY PID
PARAMETER
PID control requirements model structure very precise, and
in practical applications, to different extent, most of industrial
processes exist to the nonlinear, the variability of parameters
and the uncertainty of model, thus using conventional PID
control can not achieve the precise control of the process. But
the dependence on the mathematical model of the fuzzy
control is weak, so it isn't necessary to establish the precise
mathematical model of the process, and the fuzzy control has
a good robustness and adaptability. According to their own
characteristics, we combine fuzzy control with PID control,
and provide a based on fuzzy PID parameters self-tuning
controller with MATLAB. Fuzzy PID parameters Self-tuning
Control takes error "e" and Change-in-error "ec" as the input
of Fuzzy PID controller, meets the request of the different
moments of "e" and "ec" to PID parameters self-tuning. Using
fuzzy control rules on-line, PID parameters "kp", "ki", "kd"
are amended, which constitute a self-tuning fuzzy PID
controller, the principle of which control program as shown in
Fig. 2.
IV. IMMUNE PID CONTROL
Immune PID controller is a discrete controller based on the
principle of biological immune system.
A. Feedback Principle of Immune System
Immune is a characteristic physiological reaction of
biological body. Immune system of biology could produce
relative antibody to resist invading anti-source from
extraneous. After antibody combines with anti source, a serial
reaction will be brought to destroy antibody by swallowing
effect or producing special enzyme. In immune system, there
is a feedback mechanism that enables human survival of
infection and disease. Fig.4 presents the principle of feedback
mechanism. The basic cells that are involved in the process
are antigens Ag, antibodies Ab, B-cells B, helper T-cells TH
and suppressor T-cells TS. According to Fig.3, we know that
antigens will be recognized by APC (Antigen Presenting Cell)
when they invade into organisms, then, the message will be
sent to T-cells. After receiving the message, B-cells will be
stimulated by T-cells and create antibodies immediately to
eliminate the antigen. When the number of antigens is
increasing, the number of TH-cells will increase and the
human body can create more B-cells to protect itself. Along
with the decrease of antigens, the amount of TS-cells in the
body would increase and the number of B-cells would reduce
accordingly. After a period of time, the immune system
inclines to balance. Table 1 summarizes the regulation actions
of T-cells in the process of the above immune response.