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.
Highlights
In recent years, with the rapid economic and social development, the complexity of industry has been increasing
A block-oriented model is a description of nonlinear model, which is the result of the interaction between the dynamic linear module and static nonlinear module
Hammerstein model is a typical block-oriented model that consists of a static nonlinear block in cascade with a dynamic linear block [2]
Summary
With the rapid economic and social development, the complexity of industry has been increasing. Based on the above background, this paper discusses a new method to identify the nonlinear coefficients, linear coefficients, and fractional order of the MIMO fractional. Using AMRLS, a method for accurate parameter identification of the MIMO fractional-order model is proposed. A new recursive identification method for coefficients and fractional order of MIMO fractional-order nonlinear system with differential equation transfer function as linear block model is derived using an auxiliary model. The method in this paper solves the initial value problem of previous methods and provides more accurate initial values This initial value cooperates with AMRLS, making the result of parameters identification of multi-input and multi-output fractional Hammerstein model closer to reality.
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