Adaptive ADRC for the Fine Tracking System in Inter-satellite Laser Communication Based on Improved ESO
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摘要: 星间激光通信中的光束指向误差会显著降低链路质量. 精跟踪系统因其高带宽特性, 主要负责对高频扰动进行实时修正. 针对精跟踪环节所面临的高频扰动, 传统自抗扰控制(ADRC)在扰动估计与补偿方面仍存在性能瓶颈. 本文考虑一种特殊的干扰形式, 并基于此构造具备频率分离能力的改进扩张状态观测器, 实现对快、慢变扰动的解耦. 在此基础上提出一种融合自适应滤波的自适应ADRC框架, 该方法在传统ADRC框架基础上, 引入并联自适应滤波器, 通过滤波器权重在线更新实现对光束指向误差的自适应抑制, 提升系统在高频干扰下的控制性能. 实验结果表明, 所提方法相比传统控制方法具有更强的扰动抑制能力.Abstract: Beam pointing errors in inter-satellite laser communication can significantly degrade the link quality. The fine tracking system, characterized by its high bandwidth, is primarily responsible for real-time correction of high-frequency disturbances. However, traditional active disturbance rejection control (ADRC) exhibits limited performance in estimating and compensating high-frequency disturbances in the fine tracking stage. To address this problem, this paper considers a specific form of disturbance, based on this formulation, we construct an improved extended state observer (ESO) with frequency separation capability to decouple slow-varying and fast-varying disturbances. Based on the improved ESO, an adaptive ADRC framework incorporating an adaptive filter is proposed. The proposed method enhances the conventional ADRC framework by introducing a parallel adaptive filter, and enables adaptive suppression of the beam pointing errors through online weight updating, thereby improving the system's control performance under high-frequency disturbances. Experimental results demonstrate that the proposed method outperforms conventional control methods in disturbance rejection.
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表 1 星上主要扰源及其干扰形式
Table 1 Main disturbance sources on the satellite and their disturbance forms
扰源 干扰形式 反作用飞轮、控制力矩陀螺、动量轮 多频线谱和宽频噪声 天线等驱动机构 多频线谱和宽频噪声 三浮陀螺 宽频噪声 磁力矩器 宽频噪声 表 2 激光通信捕跟控制算法实验平台中快反镜的参数
Table 2 Parameters of fast steering mirror in experimental platform for laser communication acquisition and tracking control algorithm
方向 $ a_1 $ (1/s) $ a_2 $ (1/s2) b (10−6 rad/(s2·V)) x 1 809 $ 7.301 \times 10^6 $ $ 6.079 \times 10^6 $ $y $ (真实器件) 1 981 $ 7.487 \times 10^6 $ $ 4.736 \times 10^6 $ $y $ (控制器设计) 1 809 $ 7.301 \times 10^6 $ $ 6.079 \times 10^6 $ 表 3 实验中使用的控制器参数
Table 3 Parameters of controllers used in experiment
本文方法 PID 传统LADRC FxLMS+PID $\sigma $ $ 2\times 10^{-6} $ — — — $ k_p $ 0.43 0.43 0.95 0.43 $ k_i $ 1 339 1 339 — 1 339 $ k_d $ $ 3.5 \times 10 ^{-5} $ $ 3.5 \times 10 ^{-5} $ $ -3.5 \times 10 ^{-5} $ $ 3.5 \times 10 ^{-5} $ $ \omega_o $ 400 — 400 — μ $ \mathrm{1\times 10^{-6}} $ — — $ 5\times 10^{-8}\,(x) $, $ 3\times 10^{-6}\,(y) $ M 128 — — 128 表 4 改进ESO与传统ESO估计性能对比(V)
Table 4 Comparison of estimation performance between improved and traditional ESO (V)
最大绝对误差 平均误差 均方根误差 传统ESO x轴 0.995 −0.026 0.487 y轴 1.119 −0.041 0.492 改进ESO x轴 1.038 −0.026 0.499 y轴 1.137 −0.040 0.504 表 5 不同方法下指向误差的均方根($ 10^{-6} $rad)
Table 5 RMS of pointing error under different methods ($ 10^{-6} $rad)
控制方法 x轴 y轴 $ 30 \sim 35 $ s $ 55 \sim 60 $ s $ 30 \sim 35 $ s $ 55 \sim 60 $ s 无控制 100.26 101.10 93.15 94.13 传统LADRC 51.54 52.71 52.52 53.30 PID 31.45 34.73 25.45 27.03 FxLMS+PID 32.60 35.54 8.04 8.73 本文方法 8.08 8.04 5.30 5.35 -
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