Adaptive Evolutionary Optimization Algorithms for Simple Fuzzy Controller Tuning Dedicated to Servo Systems - Fuzzy Modeling and Control: Theory and Applications - page 164

method (Åström and Hägglund 1995 )), of the initial fuzzy control system with the
TS PI-FLC obtained by the fuzzification of the PI controller using Eq. ( 8.11 ) and
B e =

40, and of the fuzzy control systemwith the GSA-based TS PI-FLC. Figure 8.6
illustrates not only the global performance improvement in the context of Eq. ( 8.5 )but
the improvement of the control system performance reflected by reduced overshoot.

8.6 Conclusions

This chapter has focused on the adaptive GSA- and CSS-based design of TS PI-FLCs
for a class of nonlinear servo systems. Using the formulation of standard GSA and
CSS algorithms in five stages in the framework of the 5E learning cycle, a design
approach has been offered. Adaptive GSA and CSS algorithms with fixed and fuzzy
logic-based adaptive lengths have been formulated.
The results presented in this chapter are important because they represent a direc-
tion of development of adaptive evolutionary algorithms. The generalization is rel-
atively simple because Eqs. ( 8.22 ) and ( 8.36 ) are similar to the equations that char-
acterize the operating principle of other nature-inspired evolutionary optimization
algorithms. Future research will be dedicated to the generalization of the two adapta-
tion approaches discussed in this chapter to other optimization algorithms and appli-
cations principles Algorithms (Chang and Shi 2011 ; Kovács et al. 2011 ; Teodorescu
2012 ;Vascák, J., 2012 ; Filip 2012 ;Iliadisetal. 2012 ;Srivastavaetal. 2012 ; Johanyák
and Papp 2012 ; Filasova and Serbak 2012 ).

Acknowledgments This work was supported by a grant of the Romanian National Authority for
Scientific Research, CNCS - UEFISCDI, project number PN-II-ID-PCE-2011-3-0109, by a grant
in the framework of the Partnerships in priority areas - PN II program of the Romanian National
Authority for Scientific Research ANCSx, CNDI - UEFISCDI, project number PN-II-PT-PCCA-
2011-3.2-0732, and by a grant of the NSERC of Canada.

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