Fuzzy Self-Tuning Tracking Differentiator for Motion Measurement Sensors and Application in Wide-Bandwidth High-Accuracy Servo Control

Abstract

Sensor differential signals are widely used in many systems. The tracking differentiator (TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive. There is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is introduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase compensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD) capable of adaptively adjusting parameters, which uses the frequency information of the signal to achieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency information, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning designs more flexible. Simulations and experiments using motion measurement sensors show that the proposed method has good filtering performance for low-frequency signals and improves tracking ability for high-frequency signals compared to fixed-parameter differentiator.