ELEKTRONIKA IR ELEKTROTECHNIKA, ISSN 1392-1215, VOL. 19, NO. 4, 2013
Abstract—An Artificial Immune Adaptive Strategy
combining immune adaptive control and immune genetic
optimization is proposed based on the simulation of the immune
mechanisms such as the antibodies’ bifunctional structure and
the immune cells’ regulation principle. This strategy is capable
of optimizing online parameters of the immune controller with
fixed framework, better simulating the behavior of the biological
immune system. The proposed artificial immune adaptive
strategy is applied to the control of Continuous Stirred Tank
Reactor and its performance is compared with that of
Cerebellar Model Articulation Controller. The results validate
the effectiveness of artificial immune adaptive strategy.
Index Terms—Artificial immune adaptive strategy, cell
regulation, bifunctional structure, optimization, intelligent
control
I. INTRODUCTION
It is challenging to control industrial plants due to their
complicated nonlinear dynamics. In the past, many nonlinear
control methods have been developed, such as adaptive
control, robust control and variable structure control etc..
These methods rely heavily on the mathematical models of
control plants. The (over)simplifications in deriving such
models often lead to system performance degradation.
Moreover, heavy demands on mathematical analysis can be
too difficult to appreciate by many practitioners [1].
Intelligent control [2], [3] provides an alternative. The
immune systems are a kind of complicated biologically
motivated information processing system. They operate based
on the ability of the immune response to identify the intrusion
of antigens (As) and generate antibodies (Ab) to eliminate
antigens so as to maintain the dynamically balanced health of
human bodies. Therefore the immune system would be
expected to provide a new methodology for dynamic
problems and many researchers focused on the similarities
between the behavior control system and the immune system,
and have proposed many Artificial Immune Systems
Manuscript received February 24, 2012; accepted September 5, 2012.
This research is supported by Zhejiang Nature Science Foundation
(Y1110135, LY12F03018), the projects of NFSC (61203299, 61171034),
the National 863 Program of China (2011AA050201, 2011AA050204) and
Fundamental Research Funds for the Central Universities of China
(2013QNA4021).
(AIS’s) [4]–[13].
However, till now many researches focus mainly on
mimicking the capability on genetics and the optimization of
immune system [6]–[13]. Meanwhile, a little of research
paper have been publish on the immune control method or
algorithm which proposed for the imitation prototype only by
the T cell and the B cell special immune mechanism. By
mimicking T-cell feedback adjusting mechanism, Takahashi
[14] proposed an immune P controller consisting of the
activation term (to control the response speed) and the
suppression term (to stabilize the organism), but this kind of
immune controller cannot compensate for the errors from
noise and nonlinear disturbance. Takahashi [15] then
proposed an improved immune PID controller. However,
their method is not necessarily suitable for complex objects.
Ding [16]
proposed another adaptive immune controller. The
main difference was the function depicting the action between
killer T-cell and antigen is fuzzy mapping. The later immune
controllers are mostly variants of the previous immune
controllers [17]–[19].
The previous immune controller was designed mainly
based on the interactions of suppressor T-cell, helper T-cell
and killer T-cell with many simplifications and assumptions.
These simplifications and assumptions were stopgap at that
time. However, its drawbacks become more evident by
today’s standards.
In fact, the living system can maintain stability because of
both genetic optimization and the ability to adapt, i.e., the
double peculiarities of the immune action--adjusting control
and genetic optimization. This can also be seen from the
bifunctional structure of the antibody. Besides the control and
elimination of antigen, the immune system has other
behaviors such as genetic optimization [20]–[24].
Analysis 1. Antibody is essentially a bifunctional molecule
with a variable region (V-region) and a constant region
(C-region). The variation in C-region is limited. The variation
in V-region is significant, by which different antigens can be
bound. It is mostly the variation in the V-region that provides
the adaptive capability and the fast response for the immune
system [5], [21], [25]. Many earlier Immune Optimization
Algorithms were designed by simulating V-region, while
C-region is overlooked [5], [25]. On the other hand, the
immune controller has a fixed form and the characteristics of
A New Control Method Based on Artificial
Immune Adaptive Strategy
Yonggang Peng
, Xiaoping Luo
, Wei Wei
1
College of Electrical Engineering, Zhejiang University,
Hangzhou, Zhejiang, 310027, P.R. China
2
Zhejiang University City College,
Hangzhou, Zhejiang, 310015, P.R. China
pengyg@zju.edu.cn
http://dx.doi.org/10.5755/j01.eee.19.4.1246