Control of Combustion with Genetic Learning Automata

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 Automata (GLA). 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.

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