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    • 摘要: 本文提出了一种基于随机梯度优化算法的倾斜镜模型辨识方法,实现对大口径压电倾斜镜的复杂频率响应规律的辨识与控制带宽提高。文章介绍了压电倾斜镜原理和数学模型,描述了随机梯度优化算法在模型辨识的应用过程,并通过实验验证的方式检验了算法辨识模型的准确性以及在提高系统控制带宽方面的能力;最后,利用随机梯度下降算法本文还开展了对抖动输入频谱的辨识,结合倾斜镜模型的辨识结果,获得了对特定频谱区域更高抑制能力的控制效果。

       

      Abstract: Novel system identification for large aperture fast-steering mirror (FSM) is presented in this paper. Using the stochastic parallel gradient descent method (SPGD), the new system identification method is able to identify the complex piezoelectric fast-steering mirror (PZT-FSM) model exactly and greatly improve the correction effect. The principle and mathematical model of the PZT-FSM are stated briefly in the paper firstly. Then the use process of the SPGD algorithm in the system identification for the large aperture PZT-FSM is presented. By using the identified model, the validity and feasibility of the proposed approach is confirmed by our close-loop experiments. To expand the usage of the new method, the input jitter spectrum is also identified using the similar method, which enables us to get a higher correction effect for the special frequency region.