Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems

This paper focuses on the nonlinear system identification problem, which is a basic premise of control and fault diagnosis. For Hammerstein output-error nonlinear systems, we propose an auxiliary model-based multi-innovation fractional stochastic gradient method. The scalar innovation is extended to the innovation vector for increasing the data use based on the multi-innovation identification theory. By establishing appropriate auxiliary models, the unknown variables are estimated and the improvement in the performance of parameter estimation is achieved owing to the fractional-order calculus theory. Compared with the conventional multi-innovation stochastic gradient algorithm, the proposed method is validated to obtain better estimation accuracy by the simulation results.

Automated summary

This paper proposes an auxiliary model-based multi-innovation fractional stochastic gradient method for the identification problem of Hammerstein output-error nonlinear systems, which achieves improved parameter estimation performance by increasing data utilization and utilizing fractional-order calculus theory. The simulation results validate that the proposed method outperforms conventional multi-innovation stochastic gradient algorithms in terms of estimation accuracy.