• Electronics Optics & Control
  • Vol. 31, Issue 1, 104 (2024)
LI Xuhui1, LI Xudong2, WANG Jianjun1, CHENG Xiaoxiao1..., NIE Dongdong1 and WANG Guangbin3|Show fewer author(s)
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3969/j.issn.1671-637x.2024.01.017 Cite this Article
    LI Xuhui, LI Xudong, WANG Jianjun, CHENG Xiaoxiao, NIE Dongdong, WANG Guangbin. Decoupling Control of Attitude Stabilization Device for Airborne LiDAR Based on Neural Network Inverse System[J]. Electronics Optics & Control, 2024, 31(1): 104 Copy Citation Text show less

    Abstract

    During the operation of airborne LiDAR,the attitude changes of airborne platform can significantly influence the density distribution of point cloud and the accuracy of the reconstructed Digital Surface Model (DSM),so an attitude stabilization device is designed to compensate for them in real time.The designed attitude stabilization device is a nonlinear and strong coupling system.In order to eliminate the coupling effect of the attitude stabilization device and improve its motion control accuracy,a decoupling control strategy based on neural network inverse system is proposed,which obtains satisfying control effects.Firstly,the multi-variable neural network inverse model for dynamics system of the attitude stabilization device is established.Then,a PID closed-loop feedback controller and a feedforward compensator of neural network inverse system are composed to be a feedforward-feedback compound controller,to decouple the control system in real time and to improve the dynamic control performance.Finally,the decoupling control system is testified.The experimental results show that the designed decoupling control method based on neural network inverse system effectively improves the control accuracy of the attitude stabilization device,and has excellent robustness for error interference.
    LI Xuhui, LI Xudong, WANG Jianjun, CHENG Xiaoxiao, NIE Dongdong, WANG Guangbin. Decoupling Control of Attitude Stabilization Device for Airborne LiDAR Based on Neural Network Inverse System[J]. Electronics Optics & Control, 2024, 31(1): 104
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