Parameter identification of dual-rate Hammerstein-Volterra nonlinear systems by the hybrid particle swarm-gradient algorithm based on the auxiliary model

This paper aims at the parameter estimation of dual-rate Hammerstein-Volterra (DR-HV) systems. The auxiliary model (AM) method is applied to solve the incomplete identification data caused by the dual-rate sampling. Besides, combining the particle swarm optimization (PSO) and the gradient search principle, this paper proposes an improved algorithm - the hybrid particle swarm-gradient based on the auxiliary model (AM-HPSG) algorithm, which is employed to the DR-HV identification. The AM-HPSG method converts the nonlinear identification issue into the optimization problem in the parameter space. Moreover, the application of the gradient mutation strategy in the AM-HPSG algorithm can not only improve the optimization speed and the identification accuracy, but also solve the premature problem of the basic PSO method. To verify the feasibility of the AM-HPSG algorithm, the second-order DR-HV model and the third-order DR-HV models are considered in simulation. And it can be easy to find that the AM-HPSG performs much better than the PSO and the gradient iterative (GI) methods through algorithm comparison.

Automated summary

This paper introduces an improved algorithm for parameter estimation of dual-rate Hammerstein-Volterra systems. The algorithm solves the problem of incomplete identification data caused by dual-rate sampling by applying the auxiliary model method, and optimizes the nonlinear identification problem in the parameter space using a combination of particle swarm optimization and gradient search principle. Simulation experiments demonstrate that the improved algorithm performs better in terms of accuracy and speed.