Research And Application Of Filter Based On LMS (Least-Mean-Square) Algorithm

As one of the adaptive filtering algorithms,since it is introduced,the LMS (least-mean-square) algorithm has been widely used in the areas of adaptive modeling and disturbance elimination for its simple theory and efficient realization.On the basis of analyzing the process mechanism of the super-heated steam temperature,this paper proposed a signal reconstruction method of super-heated steam temperature based on the LMS algorithm.LMS filter uses a finite impulse response(FIR) model to approximate the inert zone model of super-heated system,so that the output of the filter becomes a good substitute of the inert zone output.Meantime,considering the flue gas disturbance, one of the factors that affecting the stability of superheated steam temperature,we built a heat release signal to replace the combustion disturbance signal,and made it the disturbance input of LMS filter for the disturbance elimination.Simulation results show that the algorithm complexity in this paper is low and the convergence speed is relatively fast,so it can be used effectively to model the overheated system,and verify the validity of heat signal that is constructed.