Identification of Thermal Process Using Hammerstein Model Based on Particle Swarm Optimization Algorithm

In order to identify the controlled objects which are nonlinear time-delay processes with slow time-varying in the thermal system accurately, the Hammerstein model and particle swarm optimization (PSO) algorithm were used in this paper. For the Hammerstein model discussed in this paper, the polynomial and difference equations were used to express the nonlinear part and linear part of Hammerstein model, respectively. This study used the PSO algorithm to find the optimal solution of Hammerstein model’s undetermined parameters in the parameters space. For illustration, an example of main-steam temperature system identification was utilized to show the feasibility of the Hammerstein model based on PSO algorithm in identifying the thermal system processes. The PSO-based Hammerstein model can effectively represent the controlled objects which are nonlinear time-delay processes in the thermal system and thus a class of identification problems with nonlinearity in thermal system can be solved.