Advanced Metaheuristics-Based Approach for Fuzzy Control Systems Tuning - Complex System Modelling and Control Through Intelligent Soft Computations - page 615

1 Introduction

Fuzzy logic control approach has been widely used in many successful industrial
applications. This control strategy, with the Mamdani fuzzy type inference, dem-
onstrated high robustness and effectiveness properties (Azar 2010a , b , 2012 ; Lee
1998a , b ; Passino and Yurkovich 1998 ). The known PID-type FLC structure, firstly

proposed in Qiao and Mizumoto ( 1996 ), is especially established and improved
within the practical framework (Eker and Torun 2006 ; Guzelkaya et al. 2003 ; Woo
et al. 2000 ). This particular fuzzy controller retains the characteristics similar to the
conventional PID controller and can be decomposed into the equivalent propor-
tional, integral and derivative control components (Eker and Torun 2006 ; Qiao and
Mizumoto 1996 ). In this design case, the dynamic behaviour depends on the
adequate choice of the fuzzy controller scaling factors. The tuning procedure
depends on the control experience and knowledge of the human operator, and it is
generally achieved based on a classical trials-errors procedure. There is not up to
now a systematic method to guide such a choice. This tuning problem becomes
more delicate and hard as the complexity of the control plant increases.
In order to improve further the performance of the transient and steady state
responses of the PID-type fuzzy structure, various strategies and methods are
proposed to tune their parameters. In Qiao and Mizumoto ( 1996 ) , proposed a peak
observer mechanism-based method to adjust the PID-type FLC parameters. This
self-tuning mechanism decreases the equivalent integral control component of the
fuzzy controller gradually with the system response process time. On the other
hand, Woo et al. ( 2000 ) developed a method based on two empirical functions
evolved with the system

s error information. In Guzelkaya et al. ( 2003 ), the authors
proposed a technique that adjusts the scaling factors, corresponding to the deriva-
tive and integral components, using a fuzzy inference mechanism. However, the
major drawback of all these PID-type FLC tuning methods is the dif

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cult choice of
their scaling factors and self-tuning mechanisms. The time-domain dynamics of the
fuzzy controller depends strongly on this hard choice. The tuning procedure
depends on the control experience and knowledge of the human operator, and it is
generally achieved based on a classical trials-errors procedure. Hence, having a
systematic approach to tune these scaling factors is interesting and the optimization
theory may present a promising solution.
In solving this kind of optimization problems, the classical exact optimization
algorithms, such as gradient and descent methods, do not provide a suitable solution
and are not practical. The relative objective functions are non linear, non analytical
and non convex (Bouall gue et al. 2012a , b ). Over the last decades, there has been a

growing interest in advanced metaheuristic algorithms inspired by the behaviours of
natural phenomena (Boussaid et al. 2013 ;Dr

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o et al. 2006 ; Rao and Savsani 2012 ;
Siarry and Michalewicz 2008 ). It is shown by many researchers that these algo-
rithms are well suited to solve complex computational problems in wide and var-
ious ranges of engineering applications summarized around domains of robotics,
image and signal processing, electronic circuits design, communication networks,

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