本研究針對在外部干擾和攻擊信號影響下的大型衛星編隊網絡控制系
統設計了一種基於觀察器的H∞攻擊容忍分散式編隊追踪控制策略。
首先，將攻擊信號的平滑信號模型嵌入到每顆衛星中，以避免其對
Luenberger觀測器狀態估測的破壞，並補償其對大型衛星編隊追蹤的
影響。此外，每顆衛星的基於觀測器的編隊追蹤網路控制系統必須
有效衰減來自相鄰衛星的外部干擾、測量雜訊和耦合效應之影響。
對於所提出的分散式H∞攻擊容忍基於觀察器的大型衛星編隊網路控
制系統，每一顆衛星都需要去求解一個非常複雜但解耦的Hamilton
Jacobi Isaacs方程(HJIE)。因此，針對每顆衛星採用基於HJIE
強化式學習的深度神經網絡(DNN)來直接解決相應的非線性偏微分
問題。當使用所提出的HJIE強化式Adam深度學習算法進行訓練時，
DNN可以計算出HJIE的H∞控制增益和觀察器增益以及最壞情況
下的外部干擾、測量雜訊和每顆衛星的耦合影響在離線訓練階段。也
就是說，基於HJIE強化學習算法的DNN方法可以實現強健性的分
散式H∞攻擊容忍基於觀察器的編隊控制策略。當基於HJIE強化式的
Adam學習算法收斂時，我們可以證明所提出的強化式學習DNN編隊追
蹤控制方法可以接近理論上的強健性分散式H∞攻擊容忍基於觀察
器的編隊追蹤策略。模擬中給出了一個具有外部干擾、測量雜訊和無
線通信惡意攻擊的衛星隊伍來驗證所提出之方法的有效性。

In this study, an H∞ attack-tolerant decentralized observer-based formation tracking control
strategy is designed for the network control system (NCS) of large-scale satellite team under
external disturbance and attack signal. First, smoothing signal models of attack signals are
embedded in each satellite to avoid their corruption on state estimation of Luenberger
observer and to compensate their effect on the formation tracking of large-scale satellites. In
addition, the observer-based formation tracking NCS of each satellite must efficiently
attenuate the external disturbance, measurement noise and coupling effect from adjacent
satellites. For the proposed decentralized H∞ attack-tolerant observer-based team formation
NCS of large-scale satellites, each satellite needs to solve a very complicated but decoupled
Hamilton Jacobi Isaacs equation (HJIE). Therefore, a proposed HJIE-reinforcement
learning-based deep neural network (DNN) is employed for each satellite to directly solve a
corresponding nonlinear partial differential control-observer-coupled HJIEi of decentralized
H∞ attack-tolerant observer-based formation tracking control problem. When trained by the
proposed HJIE-reinforcement Adam deep learning algorithm, DNN can be reinforced to
solve HJIEi for H∞ control gain and observer gain as well as the worst-case external
disturbance, measurement noise, and coupling of each satellite of the team formation in the
off-line training phase. That is, the proposed HJIE-reinforcement learning algorithm-based
DNN scheme in each satellite NCS can achieve robust decentralized H∞ attack-tolerant
observer-based team formation control strategy. When the HJIE-reinforcement-based Adam
learning algorithm converges, we can show that the proposed reinforcement learning-based
DNN formation tracking control scheme of each satellite can approach the theoretical robust
decentralized H∞ attack-tolerant observer-based formation tracking strategy of large-scale
satellites NCS. In the simulation example, a satellite team with external disturbance,
measurement noise and wireless communication malicious attack are given to validate the
effectiveness of proposed method separately.

摘要------------------I
Abstract-------------II
致謝------------------III
Content------------------IV
Introduction------------------1
The system dynamic model of satellite and problem formulation------------------6
HJIE-reinforcement DNN-based H∞ attack-tolerant observer-based
decentralized formation tracking control design of team formation
NCS of large-scale satellites------------------28
Simulation example------------------41
Conclusion------------------57
Reference------------------63

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