Design of Optimal Tracking Differentiator Based on Particle Swarm Optimization

Differential signals are widely used in many systems. Tracking differentiator is an efficient differential estimation method. In general, the parameter design of tracking differentiators is based on experience, it is difficult to achieve optimal performance. In this paper, an optimal parameter design method for tracking differentiators is proposed. The mathematical model between low-pass filter (LPF) and error is established. The objective function to achieve the minimum error is convex and unsolvable. Therefore, an off-line parameter design method based on particle swarm optimization (PSO) is proposed, and a new error evaluation criterion considering phase lag is proposed. Simulation and experimental results show that the optimal parameters of tracking differentiators exist and can be found by the proposed method. The proposed method is practical and can be applied to engineering efficiently.