Hammerstein identification of supercharged boiler superheated steam pressure using Laguerre-Fuzzy model

System identification plays an important role in many fields of science and engineering. This paper deals with a new nonlinear identification method fit for supercharged boiler superheated steam pressure. The superheated steam pressure is influenced by fuel and superheated steam flow, whereas traditional identification methods always omit the influence of superheated steam flow, and receive limited identification precision due to the uncertain time delay and strong nonlinearity. Taking into account that Laguerre filters can approximate linear systems (even with time delay) with a model order lower than ARX model and fuzzy identification from measured data is effective enough to approximate uncertain nonlinear systems, Laguerre filters and fuzzy model are firstly combined into Hammerstein structure to construct Laguerre-Fuzzy Hammerstein model. The defined model is a two inputs single output model which considers both of the two nonlinear variables and can avoids the decrease of identification precision resulted by uncertain time delay. The proposed model is applied in the nonlinear identification of supercharged boiler superheated steam pressure. Simulation results show that the Laguerre-Fuzzy Hammerstein model can trace the process nonlinearity precisely and has higher prediction accuracy than ARX model and basic Hammerstein model with a lower model order. Moreover, Laguerre-Fuzzy Hammerstein model improves the computation efficiency and system stability greatly.