"Intelligent Control" considers non-traditional modelling and
control approaches to nonlinear systems. Fuzzy logic, neural
networks and evolutionary computing techniques are the main tools
used. The book presents a modular switching fuzzy logic controller
where a PD-type fuzzy controller is executed first followed by a
PI-type fuzzy controller thus improving the performance of the
controller compared with a PID-type fuzzy controller.The advantage
of the switching-type fuzzy controller is that it uses one
rule-base thus minimises the rule-base during execution. A single
rule-base is developed by merging the membership functions for
change of error of the PD-type controller and sum of error of the
PI-type controller. Membership functions are then optimized using
evolutionary algorithms. Since the two fuzzy controllers were
executed in series, necessary further tuning of the differential
and integral scaling factors of the controller is then performed.
Neural-network-based tuning for the scaling parameters of the fuzzy
controller is then described and finally an evolutionary algorithm
is applied to the neurally-tuned-fuzzy controller in which the
sigmoidal function shape of the neural network is determined.
The important issue of stability is addressed and the text
demonstrates empirically that the developed controller was stable
within the operating range. The text concludes with ideas for
future research to show the reader the potential for further study
in this area.
"Intelligent Control "will be of interest to researchers from
engineering and computer science backgrounds working in the
intelligent and adaptive control."
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