臺灣博碩士論文加值系統

在以實踐人類與機器人之合作互動的前提下，本論文探討基於增強語音命令的適應分層限定時間飽和控制（SEB-AHFTSC）之全向移動機器人（ODMR）。首先，將九個語音命令萃取其特徵並輸入至多類別的支持向量機（Multiclass Support Vector Machine）進行訓練。由於背景噪音時常存在並影響語音命令的辨識，有鑑於此，故設計適當的濾波機制降低背景噪音之不利影響。文中將呈現有無背景噪音之語音命令與是否進行濾波之比較。得益於全向移動機器人能夠同時進行平移與旋轉的特色，使得基於語音命令之控制更加可靠與令人滿意。為達到更快的姿態追蹤能力， 將ODMR分成間接模式及直接模式以設計適應分層限定時間飽和控制(AHFTSC)。為了在限定時間內達成零誤差地追蹤間接輸出（即ODMR之期望姿態），設計具有非線性切換增益（Nonlinear Switching Gains）的適應限定時間虛擬期望姿態（AFTVDP）。為了使得直接輸出（即馬達電流）在限定時間內達成零誤差地追蹤AFTVDP，設計具有非線性切換增益之適應限定時間飽和控制（AFTSC）以完成語音命令的高頻率要求。
總言之，本論文所提出的「基於增強語音命令的適應分層限定時間飽和控制」包含增強語音命令之辨識與分類、AFTVDP與AFTSC。

To implement the human and robot collaborations, the speech enhancement based (SEB) adaptive hierarchical fixed-time saturated control (AHFTSC) of omnidirectional mobile robot (ODMR) is designed. From the outset, the features of nine speech commands are extracted and then trained by multiclass support vector machine. Moreover, the background noise is addressed and filtered by the suitable design of filtering mechanism. Comparisons among with or without background noise and filtering are given. Due to advantageous feature of ODMR (i.e., synchronous translation and rotation), the speech command based controls are more reliable and satisfactory. For the faster pose tracking ability, ODMR are divided as indirect and direct modes to design the AHFTSC. To null track the indirect output (desired pose of the ODMR) in fixed time, the adaptive fixed-time virtual desired pose (AFTVDP) with nonlinear switching gains is designed. To make the direct output (motor current) null track the AFTVDP in fixed time, an adaptive fixed-time saturated control (AFTSC) with nonlinear switching gains is employed to execute high frequency motions of speech command. In summary, the proposed SEB-AHFTSC contains speech enhancement’s recognition and classification, AFTVDP, and AFTSC.

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 ix
第一章 緒論 1
1-1 研究背景 1
1-2 相關文獻 3
1-3 實驗架構與任務描述 4
第二章 語音命令特徵萃取與分析 8
2.1 語音命令特徵萃取 8
2.2 語音命令特徵分析 13
第三章 適應分層限定時間飽和控制設計 18
3.1 全向移動機器人（ODMR）數學模型 18
3.2 適應分層限定時間飽和控制（AHFTSC） 20
3.3 常數切換增益與非線性切換增益之軌跡追蹤 27
3.3.1 非線性切換增益 28
3.3.2 常數切換增益 45
3.3.3 結果討論 58
第四章 語音命令之辨識與增強 59
4.1 多分類支持向量機 60
4.2 線上濾波之背景噪音消除 62
4.3 基於語音命令控制全向移動機器人（ODMR） 67
4.3.1 實驗結果 67
4.3.2 結果討論 77
4.3.3 全向移動機器人實況資料 78
第五章 結論 80
參考文獻 81

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