Recursive Identification for MIMO Fractional-Order Hammerstein Model Based on AIAGS

Abstract

In this paper, adaptive immune algorithm based on a global search strategy (AIAGS) and auxiliary model recursive least square method (AMRLS) are used to identify the multiple-input multiple-output fractional-order Hammerstein model. The model’s nonlinear parameters, linear parameters, and fractional order are unknown. The identification step is to use AIAGS to find the initial values of model coefficients and order at first, then bring the initial values into AMRLS to identify the coefficients and order of the model in turn. The expression of the linear block is the transfer function of the differential equation. By changing the stimulation function of the original algorithm, adopting the global search strategy before the local search strategy in the mutation operation, and adopting the parallel mechanism, AIAGS further strengthens the original algorithm’s optimization ability. The experimental results show that the proposed method is effective.

Convergence analysis of nonlinear model predictive control based on fractional Hammerstein model

“…22, the two outputs calculated using the GBM or NM algorithm succeed in tracking the set-point. In order to determine the effectiveness of the GBM and NM methods, we will consider a fractional MIMO system with higher order, the characteristic parameters of the Hammerstein model are identified in the article [46]. The model is as follows:…”

A New Optimization Idea: Parallel Search-Based Golden Jackal Algorithm

“…In 1995, the German scholar Stern proposed the Differential Evolution algorithm (DE) in his paper [9], which has a relatively simple structure but also possesses good optimization-seeking capabilities and can solve more complex problems in a short time. In recent years, many scholars have paid attention to the improvement of evolutionary algorithms, such as Adaptive Immune Algorithm based on a Global Search strategy (AIAGS) [10].…”