Derivation and Numerics of Integral-aided Denoising Zhang Time Derivativers

The research on time derivativer (TD, a.k.a., tracking differentiator) is a hot topic in the field of industrial automation control. In this paper, a new kind of time derivativer (called integral-aided denoising Zhang time derivativer, IDZTD) is proposed with the aid of Zhang neural dynamics (ZND). The comparative study is conducted among Han tracking differentiator (HTD, also called classic tracking differentiator), standard Zhang time derivativer (ZTD), and IDZTD. The research indicates that the IDZTD has better performance and noise immunity in the numerical experiments with different initial activation functions and design parameters. In addition, to some extent, the larger the design parameters are, the better the performance of IDZTD is. Besides, the noise immunity of IDZTD with nonlinear activation is better than that with linear activation.