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

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

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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.

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

Xiangyang Li、Rafal Madonski、Zhiqiang Gao、Senping Tian

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Key Laboratory of Autonomous Systems and Network Control,Ministry of Education,School of Automation Science and Engineering,South China University of Technology,Guangzhou 510641,China

Energy and Electricity Research Center,Jinan University,Zhuhai 519070,China

Department of Electrical and Computer Science,Cleveland State University,Cleveland,OH 44115,USA

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Guangdong ProvinceNatural Science Foundation of Guangdong Province

61773170621731512023A15150109492021A1515011850

2023

控制理论与技术(英文版)
华南理工大学

控制理论与技术(英文版)

CSCDEI
影响因子：0.307
ISSN：2095-6983
年,卷(期)：2023.21(1)
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