The application of fuzzy neural networks to the temperature control system of oil-burning tunnel kiln

Fuzzy control is a human-imitating control technique which is independent of the mathematical model of plants. It utilizes priori knowledge to carry out approximate reasoning. However, it lacks the abilities of self-tuning or self-learning in industrial applications. The temperature control process of an oil-burning tunnel kiln is a multivariable and nonlinear dynamic system. This paper presents a fuzzy neural network control strategy which is able to enhance the capacity of self-learning of fuzzy control rules, based on the self-learning ability of neural networks. Simulation research and a physical analog experiment prove the feasibility of this control strategy.