Search

• This paper provides the robust SAF-LHC-MNAG algorithm for nonlinear system identification in impulsive noise scenarios. • The proposed algorithm can accelerate the convergence under the premise of low steady-state error. • The convergence condition and the mean square performance of the SAF-LHC-MNAG algorithm are given. • The proposed algorithm is implemented to identify the hysteresis model of the piezoelectric actuator. This paper proposes a novel spline adaptive filtering (SAF) algorithm for nonlinear system identification under impulsive noise environments. This algorithm combines the logarithmic hyperbolic cosine (LHC) cost function and the modified Nesterov accelerated gradient (MNAG) learning method, which is called the SAF-LHC-MNAG algorithm. The LHC cost function can reduce the sensitivity of SAF to large outliers and improve the robustness to impulsive noises. Additionally, the MNAG method can further accelerate the convergence under the premise of low steady-state error. Performance analysis of this algorithm is carried out and supported by simulations. Numerical results show that the SAF-LHC-MNAG algorithm has better convergence performance than some existing SAF algorithms. Besides, experimental results confirm the effectiveness of SAF-LHC-MNAG for the accurate identification of nonlinear hysteresis system. [ABSTRACT FROM AUTHOR]