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基于正切Sigmoid函数的跟踪微分器
引用本文:谭诗利,雷虎民,王鹏飞. 基于正切Sigmoid函数的跟踪微分器[J]. 系统工程与电子技术, 2019, 41(7): 1590-1596. DOI: 10.3969/j.issn.1001-506X.2019.07.21
作者姓名:谭诗利  雷虎民  王鹏飞
作者单位:空军工程大学防空反导学院, 陕西 西安 710051
基金项目:国家自然科学基金(61573374,61503408);航空科学基金(20150196006)资助课题
摘    要:通过引入神经网络中的正切Sigmoid激励函数和终端吸引子函数,构造了一种非线性跟踪微分器。采用完备的稳定性理论证明了所设计跟踪微分器的渐近收敛性,并通过扫频测试分析其频域特性,总结出参数整定规则。和之前的文献相比,进一步优化了跟踪微分器的结构,整合了功能重叠的参数使得更易调参。同时,加入了终端吸引子函数降低高频信号引起的颤振使得噪声抑制能力更强。仿真结果表明,所设计的跟踪微分器具备对包含一定噪声干扰的正弦信号的滤波和求导能力,且响应速度快、精度高,具备对方波信号的快速稳定跟踪和广义导数逼近的能力,且具有更强的噪声抑制能力。

关 键 词:跟踪微分器  正切sigmoid函数  终端吸引子函数  参数整定

Design of tracking differentiator based on tangent Sigmoid function
TAN Shili,LEI Humin,WANG Pengfei. Design of tracking differentiator based on tangent Sigmoid function[J]. System Engineering and Electronics, 2019, 41(7): 1590-1596. DOI: 10.3969/j.issn.1001-506X.2019.07.21
Authors:TAN Shili  LEI Humin  WANG Pengfei
Affiliation:Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
Abstract:A nonlinear tracking differentiator is constructed by introducing the tangent Sigmoid function in the neural network and the terminal attractor function. The asymptotic convergence of the designed tracking differentiator is proved by the complete stability theory. Also, the characteristics of the frequency domain are analyzed by the frequency sweep test and the parameter tuning rules are summarized. Compared with the previous literature, the structure of the tracking differentiator is further optimized, and the parameters of overlapping functions are integrated to make it easier to adjust. At the same time, the terminal attractor function is added to reduce the flutter caused by the high frequency signal, so that the noise suppression capability is stronger. The simulation results show that the designed tracking differentiator is capable of filtering and deriving the sinusoidal signals that contain noise interference, and has the advantages of fast response speed and high precision. Simultaneously, the designed tracking differentiator achieves fast tracking and generalized derivative’s approximating of the square wave signal with stronger capabilities of noise suppression.
Keywords:tracking differentiator  tangent Sigmoid function  terminal attractor function  parameter tuning  
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