臺灣博碩士論文加值系統

在本篇論文中，我們首先探討ㄧ階的順滑模態控制設計，接著我們使用邊界層控制來消除控制輸入的顫震行為。然而，當系統存在雜訊時，上述方法無法壓制顫震行為的發生，因此我們提出二階的順滑模態控制設計，由於系統存在著未知項，因此我們提供了兩種估測方法的設計，其ㄧ是VSS/LTR observer design，其二則是uncertainty estimator design。由模擬結果，我們發現二階順滑模態控制可以有效的減少因雜訊所引起的顫震行為。最後，為了有更好的效果，我們進行三階的順滑模態的控制設計。

In the thesis, we first introduce the first-order sliding mode
control design. In order to reduce the chattering phenomenon of
control input, we propose the boundary layer control design.
However, when random noise exists in state $x$, boundary layer
control is unable to suppress the control chattering. Thus, we
introduce the second-order sliding mode control design. Because
the system exists uncertainty term, we bring up two kinds of
estimator design including the VSS/LTR observer design and the
uncertainty estimator design. The second-order sliding mode
control design can reduce the control chattering arising from
noise effectively. Finally, for better performance, we propose the
third-order sliding mode control design.

Abstract-Chinese version I
Abstract-English version II

Chapter1. Introduction 1
Chapter2. First-Order Sliding Mode Control 3
Example 6
Chapter3. Second-Order Sliding Mode Control 8
3.1 VSS/LTR observer 9
3.2 Uncertainty estimator 11
3.3 Control design 14
3.4 Example 15
Chapter4. Third-Order Sliding Mode Control 18
4.1 VSS/LTR observer 19
4.2 Control design 21
4.3 Example 23
Chapter5. Conclusion 24
References 25
Figures 27
Figure 1 The block diagram of second-order sliding mode control 27
Figure 2.1.1 Time history of system states 27
Figure 2.1.2 Control input with time 28
Figure 2.2.1 Time history of system states 28
Figure 2.2.2 Control input with time 29
Figure 2.3.1 Time history of system states 29
Figure 2.3.2 Control input with time 30
Figure 3.1.1 Time history of system states 30
Figure 3.1.2 Estimator error with time 31
Figure 3.1.3 Control variable w with time 31
Figure 3.1.4 Control input u with time 32
Figure 3.1.5 The Lyapunov function with time 32
Figure 3.2.1 Time history of system states 33
Figure 3.2.2 Estimator error with time 33
Figure 3.2.3 Control variable w with time 34
Figure 3.2.4 Control input u with time 34
Figure 3.2.5 The Lyapunov function with time 35
Figure 3.3.1 Time history of system states 35
Figure 3.3.2 Estimator error with time 36
Figure 3.3.3 Control variable w with time 36
Figure 3.3.4 Control input u with time 37
Figure 3.3.5 The Lyapunov function with time 37
Figure 3.3.6 The comparison of norm of x 38
Figure 3.4.1 Time history of system states 38
Figure 3.4.2 Estimator error with time 39
Figure 3.4.3 Control variable w with time 39
Figure 3.4.4 Control input u with time 40
Figure 3.4.5 The Lyapunov function with time 40
Figure 3.5.1 Time history of system states 41
Figure 3.5.2 Estimator error with time 41
Figure 3.5.3 Control variable w with time 42
Figure 3.5.4 Control input u with time 42
Figure 3.5.5 The Lyapunov function with time 43
Figure 4.1.1 Time history of system states 43
Figure 4.1.2 Estimator error with time 44
Figure 4.1.3 Control variable h with time 44
Figure 4.1.4 W with time 45
Figure 4.1.5 Control input u with time 45
Figure 4.1.6 The Lyapunov function with time 46
Figure 4.2.1 Time history of system states 46
Figure 4.2.2 Estimator error with time 47
Figure 4.2.3 Control variable h with time 47
Figure 4.2.4 W with time 48
Figure 4.2.5 Control input u with time 48
Figure 4.2.6 The Lyapunov function with time 49
Figure 4.3.1 Time history of system states 49
Figure 4.3.2 Estimator error with time 50
Figure 4.3.3 Control variable h with time 50
Figure 4.3.4 W with time 51
Figure 4.3.5 Control input u with time 51
Figure 4.3.6 The Lyapunov function with time 52
Figure 4.3.7 The comparison of norm of x 52

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