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E-raamat: State Estimation and Control for Low-cost Unmanned Aerial Vehicles

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 10-Jun-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319164175
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State Estimation and Control for Low-cost Unmanned Aerial VehiclesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 10-Jun-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319164175
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This book discusses state estimation and control procedures for a low-cost unmanned aerial vehicle (UAV). The authors consider the use of robust adaptive Kalman filter algorithms and demonstrate their advantages over the optimal Kalman filter in the context of the difficult and varied environments in which UAVs may be employed. Fault detection and isolation (FDI) and data fusion for UAV air-data systems are also investigated, and control algorithms, including the classical, optimal, and fuzzy controllers, are given for the UAV. The performance of different control methods is investigated and the results compared.

State Estimation and Control of Low-Cost Unmanned Aerial Vehicles covers all the important issues for designing a guidance, navigation and control (GNC) system of a low-cost UAV. It proposes significant new approaches that can be exploited by GNC system designers in the future and also reviews the current literature. The state estimation, control and FDI methods are illustrated by examples and MATLAB® simulations.

State Estimation and Control of Low-Cost Unmanned Aerial Vehicles will be of interest to both researchers in academia and professional engineers in the aerospace industry. Graduate students may also find it useful, and some sections are suitable for an undergraduate readership.

1 Introduction to Unmanned Aerial Vehicles
1(8)
1.1 Introduction
1(3)
1.2 UAV Types and Applications
4(2)
1.3 A Brief History of UAVs
6(1)
1.4 Conclusion
7(2)
References
7(2)
2 Equations of Motion for an Unmanned Aerial Vehicle
9(16)
2.1 Rigid Body Equations of Motion
9(6)
2.1.1 Coordinate Systems
9(1)
2.1.2 Derivation of Rigid Body Equations of Motion
10(5)
2.2 Orientation and Position of an Aircraft
15(1)
2.3 Small Perturbation Theory
16(2)
2.4 Linearized Equations of Motion
18(7)
2.4.1 Equations in General
18(1)
2.4.2 Characteristics of the Chosen Zagi UAV
19(1)
2.4.3 Linearized Equations of Motion for the Zagi UAV
20(3)
References
23(2)
3 Navigation Systems for Unmanned Aerial Vehicles
25(26)
3.1 Two Main Categories in Navigation
25(1)
3.2 Inertial Navigation
26(1)
3.3 Inertial Measurement Unit
27(3)
3.3.1 Rate Gyros
28(1)
3.3.2 Accelerometers
29(1)
3.4 Air Data System
30(3)
3.4.1 Air Data Measurements
31(2)
3.4.2 Derivation of the True Airspeed Components
33(1)
3.5 Surface Radar
33(2)
3.6 Altitude Measurements
35(3)
3.6.1 Types of Flight Altitudes
35(1)
3.6.2 Radio Altimeter
35(2)
3.6.3 Barometric Altimeter
37(1)
3.7 Speed-over-ground and Drift Angle Measurements via the Doppler Method
38(2)
3.8 Magnetic Measurements
40(1)
3.9 Satellite Radio Navigation
41(3)
3.9.1 GPS Structure
42(1)
3.9.2 Basic Concept of GPS
43(1)
3.10 Vision-based Systems
44(1)
3.11 Simultaneous Localization and Mapping (SLAM)
44(1)
3.12 Measurement Fault Classification and Fault Modeling
45(6)
References
48(3)
4 Estimation of Unmanned Aerial Vehicle Dynamics
51(20)
4.1 Introduction
51(1)
4.2 The Optimal Linear Discrete Kalman Filter
52(7)
4.2.1 Optimal Kalman Filter (OKF) Equations
53(2)
4.2.2 Derivation of Optimal Kalman Gain
55(2)
4.2.3 The Structure of the Kalman Filter
57(2)
4.3 Stability of the Optimal Discrete Kalman Filter
59(1)
4.4 OKF for UAV State Estimation
60(1)
4.5 Simulations
61(2)
4.6 Necessity for Kalman Filter Adaptation
63(6)
4.6.1 A Priori Uncertainty and Adaptation
63(4)
4.6.2 Innovation-Based Adaptive Estimation
67(1)
4.6.3 Residual-Based Adaptive Estimation
68(1)
4.7 Conclusion
69(2)
References
69(2)
5 Estimation of Unmanned Aerial Vehicle Dynamics in the Presence of Sensor Faults
71(24)
5.1 Introduction
71(2)
5.2 RKF with a Single Measurement Noise Scale Factor
73(2)
5.3 RKF with Multiple Measurement Noise Scale Factors
75(1)
5.4 Comparison of the R-adaptation Techniques
76(9)
5.4.1 Instantaneous Abnormal Measurements
77(2)
5.4.2 Continuous Bias of Measurements
79(2)
5.4.3 Measurement Noise Increment
81(1)
5.4.4 Fault of Zero Output
82(3)
5.5 Remark on Stability
85(6)
5.6 Conclusion and Discussion
91(4)
References
93(2)
6 Estimation of Unmanned Aerial Vehicle Dynamics in the Presence of Sensor/Actuator Faults
95(14)
6.1 Introduction
95(2)
6.2 Q-adaptation Using Multiple Adaptive Factors
97(1)
6.3 Integration Scheme for the Q- and R-adaptation Methods
98(1)
6.4 Numerical Example
99(8)
6.5 Conclusion and Discussion
107(2)
References
107(2)
7 Fault Detection, Isolation, and Data Fusion for Unmanned Aerial Vehicle Air Data Systems
109(20)
7.1 Introduction
109(2)
7.2 Kalman Filter-Based Integrated ADS/GPS Navigation System
111(3)
7.3 Federated Kalman Filter-Based Integrated ADS and GPS/INS Data
114(2)
7.3.1 Data Fusion Methodology
114(1)
7.3.2 ADS and GPS/INS Data Fusion Based on FDI
115(1)
7.4 Sensor FDI Algorithms
116(2)
7.4.1 Statistical Test for Fault Detection
116(1)
7.4.2 Fault Isolation Algorithm
117(1)
7.5 Simulation Results for Indirect Kalman Filter-Based ADS and GPS/INS Data Fusion
118(8)
7.5.1 Results with Fault Isolation
118(4)
7.5.2 Results Without Fault Isolation
122(4)
7.6 Conclusion and Discussion
126(3)
References
126(3)
8 Stability Analysis for Unmanned Aerial Vehicles
129(12)
8.1 Trimming
129(1)
8.1.1 Trim Point
129(1)
8.1.2 Linearization Around a Steady-State Condition
130(1)
8.2 Derivation of the Transfer Functions
130(7)
8.2.1 State Equations
130(5)
8.2.2 Transfer Functions
135(2)
8.3 Longitudinal Stability Analysis
137(1)
8.4 Lateral Stability Analysis
138(1)
8.5 Conclusion
139(2)
References
139(2)
9 Classic Controller Design for Unmanned Aerial Vehicles
141(30)
9.1 Classical Proportional-Integral-Derivative (PID) Controller
141(3)
9.2 Classical Controller for the Longitudinal Motion
144(14)
9.2.1 Pitch Angular Rate Controller (Inner Loop)
145(2)
9.2.2 Altitude Controller (Outer Loop)
147(7)
9.2.3 Speed Controller
154(4)
9.3 Classical Controller for the Lateral Motion
158(12)
9.3.1 Roll Rate Controller
160(2)
9.3.2 Yaw Damper
162(3)
9.3.3 Roll Angle Loop
165(1)
9.3.4 Heading Controller
166(4)
9.4 Conclusion
170(1)
References
170(1)
10 Linear Quadratic Regulator Controller Design
171(30)
10.1 Introduction
171(1)
10.2 Linear Quadratic Optimal Controller
172(4)
10.2.1 Lyapunov Stability Criteria
173(1)
10.2.2 Linear Quadratic Optimal Control Using Lyapunov Criterion
174(2)
10.3 Altitude and Speed Controller Design Using the LQR Method
176(9)
10.3.1 LQR Altitude Controller
176(1)
10.3.2 LQR Speed Controller
177(8)
10.4 LQR-Type Heading Controller
185(3)
10.5 LQR Controller with the Kalman Estimator
188(12)
10.5.1 Longitudinal LQR with the Kalman Estimator
190(5)
10.5.2 Lateral LQR with the Kalman Estimator
195(5)
10.6 Conclusion and Discussion
200(1)
References
200(1)
11 Fuzzy Logic-Based Controller Design
201(22)
11.1 Fuzzy Logic-Based Systems
201(5)
11.1.1 Mamdani-Type Fuzzy Rules
202(2)
11.1.2 Singleton-Type Fuzzy Rules
204(1)
11.1.3 Takagi--Sugeno-Type Fuzzy Rules
204(1)
11.1.4 Fuzzy Inference Mechanism
204(2)
11.2 Fuzzy Controllers
206(9)
11.2.1 Fuzzy Logic-Based Altitude and Velocity Controllers
207(3)
11.2.2 Lateral Fuzzy Logic Controller
210(5)
11.3 Stability Analysis of the Fuzzy Controllers
215(4)
11.4 A Comparison of Flight Controllers for Unmanned Aerial Vehicles (UAVs)
219(1)
11.5 Conclusion and Discussion
220(3)
References
221(2)
About the Authors 223(2)
Index 225
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