Z. Hímer (Finland), G. Dévényi (Hungary), J. Kovács, and U. Kortela (Finland)
Combustion control, non-linear systems, ANFIS,
Combustion control, Genetic Learning Automata
It is difficult to achieve effective control of time
variable and nonlinear plants such a fluidized bed
boiler. A method of designing a nonlinear fuzzy
controller is presented. However, its early application
relied on trial and error in selecting either the fuzzy
membership functions or the fuzzy rules. This made it
heavily dependent on expert knowledge, which may not
always available. Hence, an adaptive fuzzy logic
controller such as Adaptive Neuro-Fuzzy Inference
System (ANFIS) removes this stringent requirement.
This paper demonstrates the application of ANFIS a
nonlinear Multi Input Single Output fuel feeding and
combustion system and a fuzzy controller design for the
system with optimization with Genetic Learning
An ANFIS model has been developed to determine the
exact amount of fuel fed to a combustion chamber. This
property is impossible to measure directly, but it is
required for improving combustion control.
The control of the combustion base on two Takagi
Sugeno type controllers, which were optimized by
GLA. The control system has been validated on
experiment data obtained in a case-study power plant.
The results have shown that the system is able to
capture the nonlinear feature of the fuel feeding system.