Hybrid neuro-fuzzy network-priori knowledge model in temperature control of a gas water heater system

This paper presents a hybrid neuro-fuzzy network-priori knowledge model in temperature control of a gas water heater system. The hybrid model consists in a cascade connection of two blocks: an approximate first principles model (FPM) and an unknown block. The first principles model is constructed based in the balance equations of the system and in a priori knowledge. The unknown part of the global model is modeled with a neuro-fuzzy structure which is based in a priori knowledge and identified with input/output data of the system. The neuro-fuzzy hybrid model (NFHM) of the gas water gas heater consists in a cascade connection of a neuro-fuzzy model with a first principles model. The proposed hybrid model is tuned using gradient descent combined with least square algorithm off-line. A Smith predictive controller is constructed based in the water heater hybrid model. Due to the characteristics of the model, the Smith predictive control structure is simplified, linearizing the system relatively to the input gas flow and it presents a special configuration for multiple input with different time delays. Finally, the control of the output water temperature results are shown and discussed.