本文針對大規模四旋翼無人機系統在外部干擾、量測雜訊、其他相鄰四旋翼無人機的耦合以及通過無線通信對網絡控制系統(NCS)的執行器和傳感器的惡意攻擊下,提出了一種強健性分散式 H∞ 攻擊容忍基於觀測器的編隊追蹤控制方案。首先,我們建構一個攻擊信號的平滑模型來描述其行為。然後,通過將平滑模型與每個四旋翼無人機的系統狀態相結合,我們可以通過傳統的 Luenberger 觀測器同時估測攻擊信號和每個四旋翼無人機的系統狀態,從而實現大規模四旋翼無人機的高效強健性分散式 H∞ 攻擊容忍基於觀測器編隊追蹤控制。對於大規模四旋翼無人機的強健性分散式 H∞ 攻擊容忍基於觀測器編隊追蹤控制設計,必須解決一個非常困難的獨立非線性偏微分觀測器/控制器耦合的 Hamilton Jacobi Issac 方程式來進行每個四旋翼無人機的觀測器和控制器設計。現在,還沒有分析和數值方法來解決HJIE。因此,一個基於 HJIE-強化學習的深度神經網絡 (DNN)被訓練來直接解決觀測器/控制器耦合的 HJIE,用於每個四旋翼無人機的強健性分散式H∞攻擊容忍基於觀測器的編隊追蹤控制。由於四旋翼無人機的系統模型和 HJIE 已經被用於 HJIE-強化亞當學習算法的 DNN 訓練,與傳統的 DNN 大數據驅動訓練相比,我們節省了大量的訓練數據和時間,實現了強健的分散式 H∞ 攻擊容忍基於觀測器編隊追蹤控制設計。隨著亞當演算法的收斂,我們可以證明所提出的基於 HJIE-強化 DNN 的分散式 H∞ 攻擊容忍基於觀測器的控制方案可以實現理論上的結果。最後,通過模擬結果驗證所提方法的有效性。

In this paper, a robust decentralized H∞ attack-tolerant observer-based team formation tracking control scheme is proposed for large-scale quadrotor unmanned aerial vehicle (UAV) systems under external disturbance, measure noise, couplings from other neighboring quadrotor UAVs, and malicious attacks on actuator and sensor of network control system (NCS) via wireless communication. At first, we constructed a smoothed model of attack signals to describe their behavior. Then, by integrating the smoothed model with the system state of each quadrotor UAV, we can simultaneously estimate the attack signals and the system state of each quadrotor UAV through a traditional Luenberger observer for the efficient robust decentralized H∞ attack-tolerant observer-based team formation tracking control of large-scale quadrotor UAVs.
For the design of robust decentralized H∞ attack-tolerant observer-based team formation tracking control of large-scale quadrotor UAVs, a very difficult independent nonlinear partial differential observer/controller-coupled Hamilton Jacobi Issac equation must be solved for the observer and controller design of each quadrotor UAV. Nowadays, there are no analytical and numerical methods to resolve HJIE. Thus, an HJIE-reinforcement learning-based deep neural network (DNN) is trained to directly solve the observer/controller-coupled HJIE for robust decentralized H∞ attack-tolerant observer-based team formation tracking control of each quadrotor UAV. Since the system model of the quadrotor UAV and HJIE have been adopted for the HJIE-reinforcement Adam learning algorithm DNN training, compared to traditional DNN big data-driven training, we save a lot of training data and time to achieve the robust decentralized H∞ attack-tolerant observer-based team formation tracking control design. As the Adam algorithm converges, we could show that the proposed HJIE-reinforcement DNN-based decentralized H∞ attack-tolerant observer-based tracking control scheme can achieve the theoretical result. Finally, the simulation results are presented with a comparison to verify the effectiveness of the proposed method.

摘要..................................................................I
Abstract............................................................II
致謝................................................................III
Content.............................................................IV
I. Introduction......................................................1
II. System Description and Preliminaries.............................6
III. Decentralized H∞ Attack-Tolerant Observer-Based Team Formation Tracking Control of Large-Scale Quadrotor UAV NCS...................15
IV. HJIE-reinforcement DNN-Based Decentralized H∞ Attack-tolerant
Observer-based Team Formation Tracking Control Design of Large-scale
Quadrotor UAV NCS...................................................18
V. Simulation Results...............................................26
VI. Conclusion......................................................34
VII. Appendix.......................................................35
References..........................................................43

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