On a novel tracking differentiator design based on iterative learning in a moving window

作     者：Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Xiangyang Li;Rafal Madonski;Zhiqiang Gao;Senping Tian

作者机构：Key Laboratory of Autonomous Systems and Network ControlMinistry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research CenterJinan UniversityZhuhai 519070China Department of Electrical and Computer ScienceCleveland State UniversityClevelandOH 44115USA 

出 版 物：《Control Theory and Technology》 (控制理论与技术（英文版）)

年 卷 期：2023年第21卷第1期

页      面：46-55页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 

基　　金：supported by National Natural Science Foundation of China(61773170,62173151) the Natural Science Foundation of Guangdong Province(2023A1515010949,2021A1515011850). 

主　　题：Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system) 

摘      要：Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by J.Han in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the differentiation.The two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer platform.Both algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.