臺灣博碩士論文加值系統

由統計數據得知，飛安事件多數發生在民航機著陸的階段，根據美國國家運輸安全委員會在1950年至2008的研究調查指出，在所有飛機失事中，12%的意外事件與天候不良有關。航機在進場或落地階段時，因為在高度不高及速度不快的情況下，一旦遇到剪風或亂流等大氣的劇烈變化，會造成飛機航向、下滑軌跡的偏移，嚴重影響飛航安全。現今在大多數的飛機上都已安裝自動降落系統，此系統能夠在正常的飛航環境中依靠儀電降落系統幫助飛行器安全地自動降落，並且能減少飛行員的工作負擔。但傳統的自動著陸系統所採用的控制理論為增益預定或傳統適應控制的技術，一旦飛行條件或風擾強度超出系統所能適應之範圍，飛行員就必須關閉自動著陸系統改由手動駕駛接管飛機著陸程序。本文採用了以複合型小腦模型控制器來取代傳統控制器，以及使用李亞普諾理論去證明智慧型控制器的穩定性，並推導出最佳化學習率，供不同控制器使用。最後，由理論的推導和模擬的結果顯示，智慧型控制器能使飛機適應較大範圍的風擾強度，並能成功的引導飛機著陸。

According to flight records, most aircraft accidents occurred during final approach. According to a survey of the National Transportation Safety Board, 12 % of aircraft accidents in the years of 1950 to 2008 were weather related. Among these accidents, some causes are attributed to weather and human factors. When aircraft approaches landing phase the altitude is low and the speed is slow. If the aircraft encountered wind shear or turbulence while landing it could cause altitude loss, heading variation and even crash. Nowadays, most aircraft have installed the Automatic Landing System (ALS) which relies on the Instrument Landing System (ILS) to help aircraft landing safely and reduces pilot’s work loading greatly. But control schemes of the conventional ALS usually use gain-scheduling and conventional adaptive control techniques. If the flight conditions are beyond the preset envelope, the ALS is disabled and the pilot takes over. In order to improve the performance of the ALS, this paper presents several hybrid CMACs to replace conventional controller and guide the aircraft to a safe landing. Moreover, stability of the proposed automatic landing control system is guaranteed by Lyapunov theorem. Optimal learning rates are derived by convergence theorem. Finally, from theory analysis and simulation results, the proposed intelligent controllers can enable the aircraft to adapt to wide range of wind disturbances and guide the aircraft to a safe landing.

Contents

Abstract (Chinese) I
Abstract (English) II
Acknowledgement (Chinese) III
Contents IV
List of Figures VII
List of Tables X
1. Introduction 1
1.1 Research Background 1
1.2 Literatures Review 5
1.3 Research Purposes 7
1.4 Organization of This Thesis 7
2. Aircraft Landing Control System Analysis 9
2.1 Aircraft Landing System 9
2.1.1 Instrument Landing System (ILS) 10
2.1.2 Automatic Landing System Control 13
2.2 Aircraft Models 14
2.2.1 Aircraft Dynamics 15
2.2.2 Glide Slope and Flare Commands 16
2.3 Turbulence Model 19
2.4 Wind Shear Model 21
2.5 Successful Touchdown Conditions 23
3. Stability Analysis of Hybrid CMAC Controller 24
3.1 Lyapunov Theory 24
3.2 Adaptive CMAC 28
3.2.1 Structure of CMAC 29
3.2.2 Stability analysis of adaptive CMAC 30
3.3 Adaptive CMAC-GBF 33
3.3.1 Structure of CMAC-GBF 33
3.3.2 Stability analysis of adaptive CMAC-GBF 37
3.4 Adaptive Fuzzy CMAC 41
3.4.1 Structurre of Fuzzy CMAC 41
3.4.2 Stability analysis of adaptive Fuzzy CMAC 44
3.5 Adaptive Type-2 Fizzy CMAC 46
3.5.1 Structure of Type-2 Fizzy CMAC 46
3.5.2 Stability analysis of adaptive Type-2 Fizzy CMAC 49
3.6 Application of hybrid CMAC Network to ALS 54
4. Landing Control Analysis in Turbulence Condition 58
4.1 Simulations of Conventional Controller 58
4.2 Simulations of Adaptive CMAC 60
4.3 Simulations of Adaptive CMAC-GBF 63
4.4 Simulations of Adaptive Fuzzy CMAC 66
4.5 Simulations of Adaptive Type-2 Fuzzy CMAC 68
4.6 Discussions 71
5. Landing Control Analysis in Wind Shear Condition 72
5.1 Simulations of Conventional Controller 73
5.2 Simulations of Adaptive CMAC 74
5.3 Simulations of Adaptive CMAC-GBF 76
5.4 Simulations of Adaptive Fuzzy-CMAC 79
5.5 Simulations of Adaptive Type-2 Fuzzy-CMAC 81
5.6 Discussions 83
6. Conclusions 85
6.1 Conclusion 85
6.2 Future Work 86
References 87





















List of Figures
Figure 1-1 NTSB weather related accidents by weather condition 4
Figure 2-1 Glide path and flare path 10
Figure 2-2 A localizer beam system 12
Figure 2-3 A glide slope beam system 13
Figure 2-4 Conventional PID controller architecture 14
Figure 2-5 Pitch autopilot 14
Figure 2-6 Automatic landing system architecture 15
Figure 2-7 Vertical Wind Turbulence Velocity Component 19
Figure 2-8 Longitudinal Wind Turbulence Velocity Component 19
Figure 2-9 Turbulence profile 20
Figure 2-10 Profile of wind shear model 22
Figure 3-1 The CMAC control scheme 28
Figure 3-2 Conventional CMAC structure 30
Figure 3-3 The conceptual diagram of CMAC_GBF 34
Figure 3-4 Conventional Fuzzy-CMAC structure 42
Figure 3-5 Architecture of type-2 fuzzy CMAC network 47
Figure 3-6 The hybrid CMAC control scheme 54
Figure 3-7 The control process of hybrid CMAC 55
Figure 3-8 The learning process of hybrid CMAC 55
Figure 3-9 Optimal controller parameters search of the hybrid CMAC scheme 57
Figure 4-1 Turbulence profile (30 ft/sec) 60
Figure 4-2 Aircraft pitch and pitch command (30 ft/sec) 60
Figure 4-3 Vertical velocity and command (30 ft/sec) 60
Figure 4-4 Aircraft altitude and command (30 ft/sec) 60
Figure 4-5 Turbulence profile (85 ft/sec) 62
Figure 4-6 Aircraft pitch and pitch command (85 ft/sec) 62
Figure 4-7 Vertical velocity and command (85 ft/sec) 62
Figure 4-8 Aircraft altitude and command (85 ft/sec) 62
Figure 4-9 Turbulence profile (90 ft/sec) 64
Figure 4-10 Aircraft pitch and pitch command (90 ft/sec) 64
Figure 4-11 Vertical velocity and command (90 ft/sec) 65
Figure 4-12 Aircraft altitude and command (90 ft/sec) 65
Figure 4-13 Turbulence profile (100 ft/sec) 67
Figure 4-14 Aircraft pitch and pitch command (100 ft/sec) 67
Figure 4-15 Vertical velocity and command (100 ft/sec) 67
Figure 4-16 Aircraft altitude and command (100 ft/sec) 67
Figure 4-17 Turbulence profile (165 ft/sec) 70
Figure 4-18 Aircraft pitch and pitch command (165 ft/sec) 70
Figure 4-19 Vertical velocity and command (165 ft/sec) 70
Figure 4-20 Aircraft altitude and command (165 ft/sec) 70
Figure 5-1 An aircraft encounters with wind shear 72
Figure 5-2 Turbulence profile (11 ft/sec) 73
Figure 5-3 Aircraft pitch and pitch command (11 ft/sec) 73
Figure 5-4 Vertical velocity and command (11 ft/sec) 74
Figure 5-5 Aircraft altitude and command (11 ft/sec) 74
Figure 5-6 Turbulence profile (49 ft/sec) 75
Figure 5-7 Aircraft pitch and pitch command (49 ft/sec) 75
Figure 5-8 Vertical velocity and command (49 ft/sec) 76
Figure 5-9 Aircraft altitude and command (49 ft/sec) 76
Figure 5-10 Turbulence profile (60 ft/sec) 77
Figure 5-11 Aircraft pitch and pitch command (60 ft/sec) 77
Figure 5-12 Vertical velocity and command (60 ft/sec) 78
Figure 5-13 Aircraft altitude and command (60 ft/sec) 78
Figure 5-14 Turbulence profile (50 ft/sec) 80
Figure 5-15 Aircraft pitch and pitch command (50 ft/sec) 80
Figure 5-16 Vertical velocity and command (50 ft/sec) 80
Figure 5-17 Aircraft altitude and command (50 ft/sec) 80
Figure 5-18 Turbulence profile (65 ft/sec) 82
Figure 5-19 Aircraft pitch and pitch command (65 ft/sec) 82
Figure 5-20 Vertical velocity and command (65 ft/sec) 82
Figure 5-21 Aircraft altitude and command (65 ft/sec) 82














List of Tables
Table 1-1 Accidents, fatalities, and rates, 1988 through 2008 in U.S 2
Table 1-2 Causes of fatal accidents by decade (percentage), 1950 through 2008 3
Table 4-1 The results from using conventional controller 59
Table 4-2 The results from using adaptive CMAC 61
Table 4-3 The Results from Using CMAC 62
Table 4-4 The results from using adaptive CMAC_GBF with RGA 63
Table 4-5 The results from using CMAC_GBF 65
Table 4-6 The results from using adaptive Fuzzy CMAC with RGA 66
Table 4-7 The Results from Using Fuzzy-CMAC 68
Table 4-8 The results from using adaptive Type-2 fuzzy CMAC with RGA 69
Table 4-9 The results from using type-2 fuzzy CMAC with RGA 70
Table 5-1 The results from using conventional controller in wind shear environment
73
Table 5-2 The results from using adaptive CMAC with RGA controller in wind shear environment 75
Table 5-3 The results from using CMAC with RGA controller in wind shear environment 76
Table 5-4 The results from using CMAC_GBF with RGA controller in wind shear environment 77
Table 5-5 The results from using CMAC_GBF controller with RGA in wind shear environment 78
Table 5-6 The results from using adaptive Fuzzy CMAC with RGA controller in wind shear environment 79
Table 5-7 The results from using Fuzzy CMAC with RGA controller in wind shear environment 80
Table 5-8 The results from using adaptive Type-2 Fuzzy CMAC controller with RGA in wind shear environment 81
Table 5-9 The results from using type-2 fuzzy CMAC controller with RGA in wind shear environment 83
Table 5-10 The comparison of hybrid CMACs 83

Contents

Abstract (Chinese) I
Abstract (English) II
Acknowledgement (Chinese) III
Contents IV
List of Figures VII
List of Tables X
1. Introduction 1
1.1 Research Background 1
1.2 Literatures Review 5
1.3 Research Purposes 7
1.4 Organization of This Thesis 7
2. Aircraft Landing Control System Analysis 9
2.1 Aircraft Landing System 9
2.1.1 Instrument Landing System (ILS) 10
2.1.2 Automatic Landing System Control 13
2.2 Aircraft Models 14
2.2.1 Aircraft Dynamics 15
2.2.2 Glide Slope and Flare Commands 16
2.3 Turbulence Model 19
2.4 Wind Shear Model 21
2.5 Successful Touchdown Conditions 23
3. Stability Analysis of Hybrid CMAC Controller 24
3.1 Lyapunov Theory 24
3.2 Adaptive CMAC 28
3.2.1 Structure of CMAC 29
3.2.2 Stability analysis of adaptive CMAC 30
3.3 Adaptive CMAC-GBF 33
3.3.1 Structure of CMAC-GBF 33
3.3.2 Stability analysis of adaptive CMAC-GBF 37
3.4 Adaptive Fuzzy CMAC 41
3.4.1 Structurre of Fuzzy CMAC 41
3.4.2 Stability analysis of adaptive Fuzzy CMAC 44
3.5 Adaptive Type-2 Fizzy CMAC 46
3.5.1 Structure of Type-2 Fizzy CMAC 46
3.5.2 Stability analysis of adaptive Type-2 Fizzy CMAC 49
3.6 Application of hybrid CMAC Network to ALS 54
4. Landing Control Analysis in Turbulence Condition 58
4.1 Simulations of Conventional Controller 58
4.2 Simulations of Adaptive CMAC 60
4.3 Simulations of Adaptive CMAC-GBF 63
4.4 Simulations of Adaptive Fuzzy CMAC 66
4.5 Simulations of Adaptive Type-2 Fuzzy CMAC 68
4.6 Discussions 71
5. Landing Control Analysis in Wind Shear Condition 72
5.1 Simulations of Conventional Controller 73
5.2 Simulations of Adaptive CMAC 74
5.3 Simulations of Adaptive CMAC-GBF 76
5.4 Simulations of Adaptive Fuzzy-CMAC 79
5.5 Simulations of Adaptive Type-2 Fuzzy-CMAC 81
5.6 Discussions 83
6. Conclusions 85
6.1 Conclusion 85
6.2 Future Work 86
References 87





















List of Figures
Figure 1-1 NTSB weather related accidents by weather condition 4
Figure 2-1 Glide path and flare path 10
Figure 2-2 A localizer beam system 12
Figure 2-3 A glide slope beam system 13
Figure 2-4 Conventional PID controller architecture 14
Figure 2-5 Pitch autopilot 14
Figure 2-6 Automatic landing system architecture 15
Figure 2-7 Vertical Wind Turbulence Velocity Component 19
Figure 2-8 Longitudinal Wind Turbulence Velocity Component 19
Figure 2-9 Turbulence profile 20
Figure 2-10 Profile of wind shear model 22
Figure 3-1 The CMAC control scheme 28
Figure 3-2 Conventional CMAC structure 30
Figure 3-3 The conceptual diagram of CMAC_GBF 34
Figure 3-4 Conventional Fuzzy-CMAC structure 42
Figure 3-5 Architecture of type-2 fuzzy CMAC network 47
Figure 3-6 The hybrid CMAC control scheme 54
Figure 3-7 The control process of hybrid CMAC 55
Figure 3-8 The learning process of hybrid CMAC 55
Figure 3-9 Optimal controller parameters search of the hybrid CMAC scheme 57
Figure 4-1 Turbulence profile (30 ft/sec) 60
Figure 4-2 Aircraft pitch and pitch command (30 ft/sec) 60
Figure 4-3 Vertical velocity and command (30 ft/sec) 60
Figure 4-4 Aircraft altitude and command (30 ft/sec) 60
Figure 4-5 Turbulence profile (85 ft/sec) 62
Figure 4-6 Aircraft pitch and pitch command (85 ft/sec) 62
Figure 4-7 Vertical velocity and command (85 ft/sec) 62
Figure 4-8 Aircraft altitude and command (85 ft/sec) 62
Figure 4-9 Turbulence profile (90 ft/sec) 64
Figure 4-10 Aircraft pitch and pitch command (90 ft/sec) 64
Figure 4-11 Vertical velocity and command (90 ft/sec) 65
Figure 4-12 Aircraft altitude and command (90 ft/sec) 65
Figure 4-13 Turbulence profile (100 ft/sec) 67
Figure 4-14 Aircraft pitch and pitch command (100 ft/sec) 67
Figure 4-15 Vertical velocity and command (100 ft/sec) 67
Figure 4-16 Aircraft altitude and command (100 ft/sec) 67
Figure 4-17 Turbulence profile (165 ft/sec) 70
Figure 4-18 Aircraft pitch and pitch command (165 ft/sec) 70
Figure 4-19 Vertical velocity and command (165 ft/sec) 70
Figure 4-20 Aircraft altitude and command (165 ft/sec) 70
Figure 5-1 An aircraft encounters with wind shear 72
Figure 5-2 Turbulence profile (11 ft/sec) 73
Figure 5-3 Aircraft pitch and pitch command (11 ft/sec) 73
Figure 5-4 Vertical velocity and command (11 ft/sec) 74
Figure 5-5 Aircraft altitude and command (11 ft/sec) 74
Figure 5-6 Turbulence profile (49 ft/sec) 75
Figure 5-7 Aircraft pitch and pitch command (49 ft/sec) 75
Figure 5-8 Vertical velocity and command (49 ft/sec) 76
Figure 5-9 Aircraft altitude and command (49 ft/sec) 76
Figure 5-10 Turbulence profile (60 ft/sec) 77
Figure 5-11 Aircraft pitch and pitch command (60 ft/sec) 77
Figure 5-12 Vertical velocity and command (60 ft/sec) 78
Figure 5-13 Aircraft altitude and command (60 ft/sec) 78
Figure 5-14 Turbulence profile (50 ft/sec) 80
Figure 5-15 Aircraft pitch and pitch command (50 ft/sec) 80
Figure 5-16 Vertical velocity and command (50 ft/sec) 80
Figure 5-17 Aircraft altitude and command (50 ft/sec) 80
Figure 5-18 Turbulence profile (65 ft/sec) 82
Figure 5-19 Aircraft pitch and pitch command (65 ft/sec) 82
Figure 5-20 Vertical velocity and command (65 ft/sec) 82
Figure 5-21 Aircraft altitude and command (65 ft/sec) 82














List of Tables
Table 1-1 Accidents, fatalities, and rates, 1988 through 2008 in U.S 2
Table 1-2 Causes of fatal accidents by decade (percentage), 1950 through 2008 3
Table 4-1 The results from using conventional controller 59
Table 4-2 The results from using adaptive CMAC 61
Table 4-3 The Results from Using CMAC 62
Table 4-4 The results from using adaptive CMAC_GBF with RGA 63
Table 4-5 The results from using CMAC_GBF 65
Table 4-6 The results from using adaptive Fuzzy CMAC with RGA 66
Table 4-7 The Results from Using Fuzzy-CMAC 68
Table 4-8 The results from using adaptive Type-2 fuzzy CMAC with RGA 69
Table 4-9 The results from using type-2 fuzzy CMAC with RGA 70
Table 5-1 The results from using conventional controller in wind shear environment
73
Table 5-2 The results from using adaptive CMAC with RGA controller in wind shear environment 75
Table 5-3 The results from using CMAC with RGA controller in wind shear environment 76
Table 5-4 The results from using CMAC_GBF with RGA controller in wind shear environment 77
Table 5-5 The results from using CMAC_GBF controller with RGA in wind shear environment 78
Table 5-6 The results from using adaptive Fuzzy CMAC with RGA controller in wind shear environment 79
Table 5-7 The results from using Fuzzy CMAC with RGA controller in wind shear environment 80
Table 5-8 The results from using adaptive Type-2 Fuzzy CMAC controller with RGA in wind shear environment 81
Table 5-9 The results from using type-2 fuzzy CMAC controller with RGA in wind shear environment 83
Table 5-10 The comparison of hybrid CMACs 83