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| On a novel tracking differentiator design based on iterative learning in a moving window |
| XiangyangLi1,RafalMadonski2,ZhiqiangGao3,SenpingTian1 |
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| (1 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;2 Energy and Electricity Research Center, Jinan University, Zhuhai 519070, China;3 Department of Electrical and Computer Science, Cleveland State University, Cleveland, OH 44115, USA) |
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| 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. |
| 关键词: Tracking differentiator (TD) · Iterative learning · Iterative learning tracking differentiator (IL-TD) · Active disturbance rejection control (ADRC) · Two-dimensional system (2-D system) |
| DOI：https://doi.org/10.1007/s11768-023-00132-3 |
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| 基金项目:This work was supported by National Natural Science Foundation of China (61773170, 62173151) and the Natural Science Foundation of Guangdong Province (2023A1515010949, 2021A1515011850). |
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| On a novel tracking differentiator design based on iterative learning in a moving window |
| Xiangyang Li1,Rafal Madonski2,Zhiqiang Gao3,Senping Tian1 |
| (1 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;2 Energy and Electricity Research Center, Jinan University, Zhuhai 519070, China;3 Department of Electrical and Computer Science, Cleveland State University, Cleveland, OH 44115, USA) |
| Abstract: |
| 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. |
| Key words: Tracking differentiator (TD) · Iterative learning · Iterative learning tracking differentiator (IL-TD) · Active disturbance rejection control (ADRC) · Two-dimensional system (2-D system) |
