Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle

In this paper, we use the hierarchical identification principle to decompose a Hammerstein controlled autoregressive system into three subsystems, apply the key term separation principle to express the system output as a linear combination of the system parameters, and then derive a hierarchical gradient parameter estimation algorithm for identifying all subsystems. Finally, a multi-innovation stochastic gradient algorithm is presented to improve the estimation accuracy by making full of the identification innovation. The simulation results show that the proposed algorithm is effective.