High-order Sliding Mode Tracking Differentiator with Neural Network based Adaptive Parameter Estimation

Abstract

Abstract This paper presents a new high-order sliding mode tracking differentiator with backpropagation neural network based adaptive parameter estimation (SMF-BP) to obtain accurate filtering and differentiation estimates from signals that contain noise. It is improved based on the Levant’s high-order sliding mode tracking differentiator. First, SMF-BP incorporates a sigmoid function to reduce overshoot. Second, the BP neural network is introduced to adjust the parameters adaptively to balance response speed and filtering performance. Finally, the validity of SMF-BP has been demonstrated through numerical simulation examples.