為了在室內環境（例如：辦公室，圖書館，機場，酒店，醫院，倉庫區域）中實現特定人士之追隨，首先採用了使用超寬頻（UWB）系統並依賴其擁有的雙向響應的分散式無線傳感器節點，通過雙向長短期記憶模型（Bi-LSTM）實現無線定位。即作為分佈式UWB實時定位系統(DUWB-RTLS)。由於全向服務機器人（ODSR）的有利功能，設計了基於遞迴神經網絡的有限時間追蹤控制（RNNE-FTTC），以使ODSR透過DUWB-RTLS來追蹤特定人士。為了在有限時間內實現ODSR的零姿態追隨誤差，設計了遞迴神經網絡有限時間參考軌跡（RNN-FTRT），使得基於神經網絡的有限時間跟踪控制（RNN-FTTC）間接追隨由DUWB-RTLS提供的參考姿態。 Lyapunov穩定性理論驗證了所提出的遞迴神經網絡有限時間跟踪控制（RNNE-FTTTC）包括RNN-FTRT和RNN-FTTC的穩定性和性能分析。總結來說，提出的DUWB-RNNE-FTTC包括DUWB-RTLS和RNNE-FTTC。最後，以動態定位，軌跡追蹤，無線導引和特定人員之追隨，驗證所提出的控制方法的可行性、有效性和強健性。

To implement the specific human following in an indoor environment (e.g., office, library, airport, hotel, hospital, inventory region), the distributive wireless sensor nodes using ultra- wideband (UWB) system are first employed to achieve wireless localization through bi-long-short term memory (Bi-LSTM) model owing to its two-way response dependency. It is namely as a distributive UWB real-time localization system (DUWB-RTLS). Due to advantageous feature of omnidirectional service robot (ODSR), the recurrent neural network enhanced finite-time tracking control (RNNE-FTTC) is designed such that the ODSR tracks specific human through the DUWB-RTLS. To achieve the zero pose following error of ODSR in finite time, a recurrent neural network finite-time reference trajectory (RNN-FTRT) is designed such that the neural network based finite-time tracking control (RNN-FTTC) indirectly follows the reference pose provided by DUWB-RTLS. The stability and performance analyses of the proposed RNNE-FTTC including RNN-FTRT and RNN-FTTC are verified by Lyapunov stability theory. In summary, the proposed DUWB-RNNE-FTTC includes DUWB-RTLS and RNNE-FTTC. Finally, the implementations of trajectory tracking, dynamic localization, wireless navigation, and specific human following validate the feasibility, effectiveness, and robustness of the proposed control approach.

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