New identification method for Hammerstein models based on approximate least absolute deviation

Disorder and peak noises or large disturbances can deteriorate the identification effects of Hammerstein non-linear models when using the least-square (LS) method. The least absolute deviation technique can be used to resolve this problem; however, its absolute value cannot meet the need of differentiability required by most algorithms. To improve robustness and resolve the non-differentiable problem, an approximate least absolute deviation (ALAD) objective function is established by introducing a deterministic function that exhibits the characteristics of absolute value under certain situations. A new identification method for Hammerstein models based on ALAD is thus developed in this paper. The basic idea of this method is to apply the stochastic approximation theory in the process of deriving the recursive equations. After identifying the parameter matrix of the Hammerstein model via the new algorithm, the product terms in the matrix are separated by calculating the average values. Finally, algorithm convergence is proven by applying the ordinary differential equation method. The proposed algorithm has a better robustness as compared to other LS methods, particularly when abnormal points exist in the measured data. Furthermore, the proposed algorithm is easier to apply and converges faster. The simulation results demonstrate the efficacy of the proposed algorithm.