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基于双前馈+双神经网络自适应快速反射镜的解耦控制 被引量:4

Decoupling control of fast steering mirror based on dual feedforward +dual neural network adaptive
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摘要 基于柔性铰链结构支撑和音圈电机驱动的两轴快速反射镜是一个两输入两输出强耦合系统,X轴和Y轴间的耦合大幅降低了反射镜的定位精度,采用传统的PID控制算法很难实现高精度的解耦控制。针对中心对称和轴对称结构形式的两轴快速反射镜,理论分析了两轴快速反射镜耦合来源—直流耦合分量和非直流耦合分量;建立了X轴和Y轴间的耦合物理模型;提出的双前馈+双神经网络自适应解耦控制算法分别补偿直流耦合分量和非直流耦合分量。实验结果表明:该控制算法与传统的PID控制算法相比,耦合度从5%左右降低到1.0‰以内,从而定位精度从2.5%左右提高到0.5‰以内。 Two-axis fast steering mirror based on flexure hinge support and voice coil motor drive is a strong coupling system with two inputs and two outputs.The coupling between X-axis and Y-axis greatly reduces the positioning accuracy of the fast steering mirror.It is difficult to achieve high precision decoupling control by using traditional PID control algorithm.Based on the centrosymmetric and axisymmetric two-axis fast steering mirror,the coupling sources of the two-axis fast steering mirror—DC coupling component and non-DC coupling component were analyzed theoretically,and the coupling physical model of between X-axis and Y-axis was established.A dual feedforward + dual neural network adaptive decoupling control algorithm was proposed to respectively compensate DC coupling components and non-DC coupling components.Experimental results show that,compared with the traditional PID control algorithm,the coupling degree of the proposed algorithm is reduced from about 5% to less than 1.0‰,which significantly improves the positioning accuracy from about 2.5%to less than 0.5‰.
作者 王瑞 苏秀琴 乔永明 吕涛 王轩 王凯迪 Wang Rui;Su Xiuqin;Qiao Yongming;Lv Tao;Wang Xuan;Wang Kaidi(Key Laboratory of Space Precision Measurement Technology,Xi’an Institute of Optics and Precision Mechanics,Chinese Academy of Sciences,Xi’an 710119,China;University of Chinese Academy of Sciences,Beijing 100049,China;Pilot National Laboratory for Marine Science and Technology,Qingdao 266237,China)
出处 《红外与激光工程》 EI CSCD 北大核心 2021年第11期221-227,共7页 Infrared and Laser Engineering
基金 中国科学院战略高技术创新项目(GQRC-19-23)。
关键词 两轴快速反射镜 双前馈 双神经网络自适应 解耦 定位精度 two-axis fast steering mirror dual feedforward dual neural network adaptive decoupling positioning accuracy
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