《Fully Tuned Radial Basis Function Neural Networks for Flight Control》
《Fully Tuned Radial Basis Function Neural Networks for Flight Control》调整径向基函数神经网络的飞行控制
作者：
N. Sundararajan
P. Saratchandran
Van Li
出版社：Springer
出版时间：2002年
目录
Preface xi
1. A REVIEW OF NONLINEAR ADAPTIVE NEURAL CONTROL
SCHEMES 1
1.1 Adaptive Control of Nonlinear Systems Using Artificial Neural
Networks 1
1.1.1 Introduction 1
1.1.2 An Overview of Neural Networks in Adaptive Control 4
1.1.3 Nonlinear System Identification and Indirect Adaptive
Control 8
1.1.4 Direct Adaptive Control Strategy 9
1.2 A Review of Neuro-Flight-Control Strategies 12
1.2.1 Autopilot Neural Flight Control Systems 14
1.2.2 Fault Tolerant Neural Flight Control System 15
1.2.3 High Angle of Attack Supermaneuver 17
1.3 Radial Basis Function Neural Network 18
1.4 Aircraft Flight Control Applications Using RBF Network 21
Part I Nonlinear System Identification and Indirect Adaptive Control
Schemes
2. NONLINEAR SYSTEM IDENTIFICATION USING LYAPUNOVBASED
FULLY TUNED RBFN 29
2.1 Introduction 29
2.2 Stable Identification Using Lyapunov-Based Fully Tuned RBF
Network 30
2.2.1 Identification Strategy and System Error Dynamics 30
2.2.2 Stable Parameter Tuning Rules 32
2.2.3 Implementation of the Tuning Rule 34
2.2.4 GRBF Network and Dead Zone Design 36
2.3 Simulation Results 38
vi i i FULLY TUNED RBF NEURAL NETWORK FOR FLIGHT CONTROL
2.3.1
2.3.2
Example 1: Identification of SISO Nonlinear System
Example 2: Identification of Nonlinear Time-Varying
Missile System
2.4 Summary
3. REAL-TIME IDENTIFICATION OF NONLINEAR SYSTEMS
USING MRANIEMRAN ALGORITHM
3.1 Introduction
3.2 Introduction of MRAN Algorithm
3.3 Performance of the MRAN Algorithm
3.3.1 The ONSAHL Algorithm
3.3.2 BM-1: Nonlinear SISO Time-Invariant System
3.3.3 BM-2: Nonlinear SISO Time-Varying System
3.4 Real-Time Implementation ofMRAN and the Extended MRAN
Algorithm
3.4.1 Analysis of MRAN for Real-Time Implementation
3.4.2 BM-3: Nonlinear MIMO Time-Invariant System
3.4.3 Extended MRAN (EMRAN) Algorithm
3.5 Performance Comparison of MRAN vs. EMRAN
3.5.1 BM-2: Nonlinear SISO Time-Varying System
3.5.2 BM-3: MIMO Nonlinear Dynamic System
3.6 Summary
4. INDIRECT ADAPTIVE CONTROL USING FULLY TUNED RBFN 69
4.1 Neural Network Based Indirect Adaptive Control 69
4.2 Neural Network Controller with Off-Line Training and On-Line
Control 71
4.2.1 Linearized Longitudinal F8 Aircraft Model 71
4.2.2 Evolution of Off-Line Training 72
4.2.3 Off-Line Training/On-Line Control for the Linearized
F8 Aircraft Model 76
4.3 On-Line Learning/On-Line Control for the Linearized Aircraft
Model 77
4.4 Summary 79
Part II Direct Adaptive Control Strategy and Fighter aircraft applications
5. DIRECT ADAPTIVE NEURO FLIGHT CONTROLLER USING
FULLY TUNED RBFN 85
5.1 Overview 85
5.2 Problem Formulation 86
5.3 Stable Tuning Rule Using Fully Tuned RBFN 87
5.3.1 Control Strategy 87
Contents ix
5.3.2 RBFN Approximation and Error Dynamics 89
5.3.3 Stable Adaptive Tuning Rule for Fully Tuned RBFN 90
5.4 Robustness Analysis 91
5.5 Implementation of the Tuning Rule 93
5.6 Summary 94
6. AIRCRAFT FLIGHT CONTROL APPLICATIONS USING
DIRECT ADAPTIVE NFC 95
6.1 Robust NFC for a Linearized F8 Aircraft Model 95
6.2 NFC for Localized Nonlinear Aircraft Model 107
6.2.1 Localized Nonlinear Fighter Aircraft Model 107
6.2.2 Performance Evaluation of the NFC 108
6.2.3 Discussion 111
6.3 NFC for Full-Fledged Nonlinear 6-DOF Aircraft Model 111
6.3.1 Full-Fledged Nonlinear 6-DOF Aircraft Model 112
6.3.2 Kinematic and Navigation Equations 113
6.3.3 Auxiliary Equations 115
6.3.4 Other Equations 116
6.3.5 High a Stability-Axis Roll Maneuver 119
6.3.6 Simulation Studies 121
6.4 Summary 125
7. MRANNEURO-FLIGHT-CONTROLLERFORROBUST AIRCRAFT
CONTROL 127
7.1 Problem Formulation and Conventional Controller 127
7.2 Robust MRAN-Flight-Controller 128
7.3 Simulation Results 130
7.3.1 Response to Model Error 131
7.3.2 Response to Actuator Sluggishness 136
7.4 Summary 139
8. CONCLUSIONS AND FUTURE WORK
8.1 Conclusions
8.2 Recommendations for Further Research
Bibliography
Topic Index
专业书籍
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