Equivalent acceleration feedforward based on sensor optimization and robust prediction

Xia Wenqiang; He Qiunong; Duan Qianwen; Zhou Xi; Deng Jiuqiang; Mao Yao

doi:10.12086/oee.2021.210153

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Xia W Q, He Q N, Duan Q W, et al. Equivalent acceleration feedforward based on sensor optimization and robust prediction[J]. Opto-Electron Eng, 2021, 48(11): 210153. doi: 10.12086/oee.2021.210153

Citation:

Xia W Q, He Q N, Duan Q W, et al. Equivalent acceleration feedforward based on sensor optimization and robust prediction[J]. Opto-Electron Eng, 2021, 48(11): 210153. doi: 10.12086/oee.2021.210153

Equivalent acceleration feedforward based on sensor optimization and robust prediction

1.
Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
2.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

Fund Project: National Natural Science Foundation of China (61905253)

More Information

^*Corresponding author: Mao Yao, E-mail: maoyao@ioe.ac.cn

Received Date 12 May 2021

Revised Date 29 November 2021

Published Date 30 November 2021

Abstract

Abstract

In a class of photoelectric tracking systems with only the target image sensor and MEMS accelerometer installed, an equivalent acceleration feedforward control method can improve the tracking ability of the system effectively. However, due to the low-frequency noise of accelerometers, the delay of the synthetic trajectory and the uncertainty of the movement model, the tracking accuracy will be limited. Therefore, in this paper, an equivalent acceleration feedforward method based on sensor optimization and robust prediction is proposed to further improve the tracking ability of the system. The frequency-domain fusion using the accelerometer measurement and the calculated acceleration can optimize the low-frequency performance of the accelerometer. Meanwhile, the robust prediction algorithm can reduce the impact of the delay of the synthetic trajectory and the uncertainty of the movement model and give more accurate feedforward acceleration. The experiment shows that this method can further improve the tracking ability of 0.1 Hz~ 4.5 Hz.
- equivalent acceleration feedforward /
- sensor optimization /
- robust prediction /
- photoelectric tracking system

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References

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Overview

Overview

Overview: Photoelectric tracking systems are extensively applied in aerospace, astronomical observation, target tracking, adaptive optics, and other scientific fields. The image sensor CCD (Charge-coupled device) used to detect the target usually has a non-negligible time delay, which severely limits the closed-loop bandwidth and tracking ability of the system. The composite control method based on input feedforward can theoretically improve the tracking ability of the system without changing the stability of the system. Therefore, this method is the main method used in actual engineering. To improve the tracking ability of a class of photoelectric tracking systems with only the target image sensor and accelerometer installed, this paper proposes an equivalent acceleration feedforward control method based on sensor optimization and robust prediction filtering. The ideal feedforward control requires real-time and accurate state information of the target, such as position, velocity, and acceleration. However, CCD can only provide the time-delayed target line-of-sight (LOS) error. To obtain the global trajectory information of the target relative to the tracking platform, additional sensors are needed to provide the position information of the tracking platform itself. As the accelerometer has a phenomenon of "noise submergence" at low-frequency, it will seriously affect the accuracy of synthetic target trajectory at low-frequency. Since the acceleration calculated by the system acceleration model is more accurate at low-frequency, a method of frequency-domain fusion using the calculated acceleration and the accelerometer measurement is proposed. In this method, the low-frequency information of the acceleration calculated by the acceleration model and the mid-and-high-frequency information of the accelerometer measurement data are combined for frequency-domain fusion, and a more accurate platform acceleration in low-frequency is obtained. Then, the fused acceleration and the LOS error detected by the CCD are used to synthesize the target trajectory. Meanwhile, considering the uncertainty of the target motion model and the time delay of the synthetic target trajectory, this paper proposes to use a robust prediction filtering method to replace the traditional Kalman filtering method to predict the target acceleration. The design method and recursive procedure of the proposed robust prediction filter are given in detail. To verify the effectiveness of the proposed method, this paper designs verification experiments in a two-axis photoelectric tracking experimental system. The experimental results show that using the target trajectory synthesized by optimized accelerometer data to achieve equivalent acceleration feedforward can effectively improve the tracking ability of 0.1 Hz~3.5 Hz. Using the robust prediction filtering method to replace the Kalman filter to predict the target acceleration can further improve the tracking ability of 0.1 Hz~4.5 Hz. Therefore, the proposed equivalent acceleration feedforward control method based on sensor optimization and robust prediction filtering can effectively improve the tracking ability of the photoelectric tracking system concerned by this article.