在這項研究中，針對外部干擾和通信耦合下的大型雙足機器人，提出了一種分散的 H∞ 時變隊形跟踪控制。為避免求解大型雙足機器人分佈式H∞團隊編隊跟踪控制的一組非線性偏微分Hamiton Jacobi Issac方程（HJIE），通過Adam學習算法訓練深度神經網絡對每個雙足機器人求解HJIE在團隊中實現分散的H∞團隊編隊跟踪控制設計。在離線訓練階段，利用來自其​​他雙足機器人的最壞情況外部擾動和最壞情況耦合代替真實的外部擾動和耦合，生成雙足機器人下一步訓練的系統狀態，不影響分散的H ∞ 隊形跟踪表現。由於HJIE和雙足機器人系統模型已被用於HJIE嵌入式DNN的訓練，與傳統的圖像大數據驅動設計相比，我們節省了大量的訓練數據和時間來實現分散的H∞團隊編隊跟踪控制設計。分類和語音識別。我們可以證明，所提出的基於 HJIE 嵌入式 DNN 的隊形控制方案在 Adam 學習算法收斂後可以接近理論上的大型雙足機器人分佈式 H∞ 隊形跟踪控制策略。最後，給出了 24 個雙足機器人時變組隊的仿真實例，以驗證所提出的基於分佈式 H∞ DNN 的大型雙足機器人組隊在外部干擾和通信耦合下的組隊跟踪性能。

In this study, a decentralized H∞ time-varying team formation tracking control is proposed to large-scale biped robots under external disturbance and communication coupling. In order to avoid solving a set of nonlinear partial differential Hamiton Jacobi Issac equations (HJIEs) of decentralized H∞ team formation tracking control of large-scale biped robots, a deep neural network is trained by Adam learning algorithm to solve HJIE for each biped robot in team to achieve the decentralized H∞ team formation tracking control design. In the off-line training phase the worst-case external disturbance and worst-case coupling from other biped robots are used to replace real external disturbance and coupling to generate the system state of biped robot for the next training step without influence on the decentralized H∞ team formation tracking performance. Since the HJIE and system model of biped robot have been employed for the training of HJIE-embedded DNN, we save much amount of training data and time to achieve decentralized H∞ team formation tracking control design than the conventional big-data driven designs in image classification and speech recognition. We could prove that the proposed HJIE-embedded DNN-based team formation control scheme can approach the theoretical decentralized H∞ team formation tracking control strategy of large-scale biped robots after the convergence of Adam learning algorithm. Finally, a simulation example of time-varying team formation of 24 biped robots is given to validate the team formation tracking performance of the proposed decentralized H∞ DNN-based team formation of large-scale biped robots under external disturbance and communication couplings.

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