Parameter identification of Hammerstein-Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization

This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particle swarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particle swarm optimization and the iterative identification technique to realize the simultaneous estimation of all parameters and the unknown time delay. Finally, parameters in the linear submodule, nonlinear submodule and the time delay are separated from the optimum parameter. Moreover, two illustrative examples are exhibited to evaluate the effectiveness of the proposed method. The simulation results demonstrate that the derived method has fast convergence speed and high estimation accuracy for estimating H-W systems with unknown time delay, and it is applied to the identification of the bed temperature systems. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a method for parameter estimation of Hammerstein-Wiener nonlinear systems with unknown time delay. The linear variable weight particle swarm method is used to transform the nonlinear system identification problem into a function optimization problem in the parameter space. By utilizing the parallel searching ability of particle swarm optimization and iterative identification technique, all parameters and the unknown time delay can be simultaneously estimated. The simulation results demonstrate the fast convergence speed and high estimation accuracy of the proposed method for H-W systems with unknown time delay, and its application in bed temperature system identification.