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E-raamat: Fundamentals of Aerospace Navigation and Guidance

(University of Michigan, Ann Arbor), (University of Michigan, Ann Arbor)
  • Formaat: EPUB+DRM
  • Sari: Cambridge Aerospace Series
  • Ilmumisaeg: 29-Aug-2014
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316147030
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Fundamentals of Aerospace Navigation and GuidanceSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Cambridge Aerospace Series
  • Ilmumisaeg: 29-Aug-2014
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316147030
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"This text covers fundamentals used in the navigation and guidance of modern aerospace vehicles, in both atmospheric and space flight. It can be used as a textbook supporting a graduate level course on aerospace navigation and guidance, a guide for self-study, or a resource for practicing engineers and researchers. It begins with an introduction that discusses why navigation and guidance ought to be considered together and delineates the class of systems of interest in navigation and guidance. The book then presents the necessary fundamentals in deterministic and stochastic systems theory and applies them to navigation. Next, the book treats optimization and optimal control for application in optimal guidance. In the final chapter, the book introduces problems where two competing controls exercise authority over a system, leading to differential games. Fundamentals of Aerospace Navigation and Guidance features examples illustrating concepts and homework problems at the end of all chapters"--

Arvustused

'The theory and applications of optimization and optimal guidance are well presented, followed by an interesting section on differential game theory accompanied by several classical examples The authors develop the equations for various problems in navigation and guidance to lead readers through the necessary thought process to develop their applications This book is appropriate for seniors, graduate students, or professionals wanting to gain an understanding of these complex topics.' D. B. Spencer, Choice 'It is a pleasure for me to review this book written by [ Professor] Kabamba and [ Professor] Girard, and, as a former Ph.D. student of the late [ Professor] Kabamba, it is also an honor The material is presented in quintessential Kabamba fashion: simple and elegant. The key ideas are outlined very clearly at the end of every chapter so that the reader does not get lost in the details of the treatment this book is an important addition to the topic of applied modern control systems, especially given the push toward greater autonomy for robotic systems in the near future. The authors have done an admirable job of piecing together the most important results from linear systems and optimal control theory in a clear and compact fashion and have shown the power of these methods via their application to aerospace navigation and guidance.' Suman Chakravorty, IEEE Systems Control Magazine

Muu info

This text covers fundamentals in navigation of modern aerospace vehicles. It is an excellent resource for both graduate students and practicing engineers.
Preface xv
1 Introduction 1(13)
1.1 Purpose and Motivation
1(1)
1.2 Problem Statement
2(1)
1.3 Scope of the Book
3(3)
1.3.1 Systems Theory
3(2)
1.3.2 Control Theory
5(1)
1.3.3 Aerospace Applications
6(1)
1.4 Examples
6(6)
1.4.1 Transoceanic Jetliner Flight
6(1)
1.4.2 Intelligence, Surveillance, and Reconnaissance with Unmanned Aerial Vehicle
6(1)
1.4.3 Homing Guidance of Heat-Seeking Missile
7(2)
1.4.4 Spacecraft Orbital Maneuvers
9(1)
1.4.5 Interplanetary Travel
9(3)
1.5 Content of the Book
12(1)
1.6 Summary of Key Results
12(1)
1.7 Bibliographic Notes for Further Reading
12(1)
1.8 Homework Problems
12(2)
2 Deterministic Systems Theory 14(34)
2.1 Linear Dynamic Systems
14(12)
2.1.1 System Linearization
14(7)
2.1.2 Properties of Linear Dynamic Systems
21(5)
2.2 Observability
26(4)
2.3 Time Invariant Systems
30(7)
2.3.1 Stability of Linear Time Invariant Systems
33(1)
2.3.2 BIBO Stability of Linear Time Invariant Systems
34(1)
2.3.3 Observability of Linear Time Invariant Systems
35(2)
2.4 The Method of Adjoints
37(2)
2.5 Controllability and Duality
39(3)
2.6 Summary of Key Results
42(1)
2.7 Bibliographic Notes for Further Reading
42(1)
2.8 Homework Problems
42(6)
3 Stochastic Systems Theory 48(30)
3.1 Probability Spaces
49(2)
3.2 Random Variables and Distributions
51(6)
3.3 Expected Value and Characteristic Function
57(2)
3.4 Independence and Correlation
59(1)
3.5 The Gaussian Distribution
59(3)
3.6 Random Processes
62(5)
3.7 Gauss-Markov Processes
67(2)
3.8 Linear Gauss-Markov Models
69(3)
3.9 Summary of Key Results
72(1)
3.10 Bibliographic Notes for Further Reading
72(1)
3.11 Homework Problems
72(6)
4 Navigation 78(40)
4.1 Position Fixing: The Ideal Case
78(2)
4.2 Position Fixing: Error Analysis
80(4)
4.3 Position Fixing: Redundant Measurements
84(3)
4.4 Examples of Fixes
87(6)
4.5 Inertial Navigation
93(5)
4.5.1 Inertially Stabilized Inertial Navigation Systems
94(1)
4.5.2 Strapped Down Inertial Navigation Systems
95(3)
4.6 Asymptotic Observers
98(2)
4.7 The Kalman Filter
100(5)
4.8 The Extended Kalman Filter
105(2)
4.9 Clock Corrections
107(1)
4.10 Navigation Hardware
108(3)
4.11 Summary of Key Results
111(1)
4.12 Bibliographic Notes for Further Reading
111(1)
4.13 Homework Problems
112(6)
5 Homing Guidance 118(32)
5.1 Fundamentals of Homing
118(3)
5.2 Pursuit Guidance
121(8)
5.2.1 Terminal Phase Analysis
123(1)
5.2.2 Approximate Miss Distance Analysis
124(2)
5.2.3 Exact Miss Distance Analysis
126(3)
5.3 Fixed Lead Guidance
129(1)
5.4 Constant Bearing Guidance
129(1)
5.5 Proportional Navigation
129(1)
5.6 Linearized Proportional Navigation
130(14)
5.6.1 Miss due to Launch Error
135(3)
5.6.2 Miss due to Step Target Acceleration
138(1)
5.6.3 Miss due to Target Sinusoidal Motion
139(1)
5.6.4 Miss due to Noise
140(1)
5.6.5 Use of Power Series Solution
141(3)
5.7 Beam Rider Guidance
144(2)
5.8 Summary of Key Results
146(1)
5.9 Bibliographic Notes for Further Reading
146(1)
5.10 Homework Problems
147(3)
6 Ballistic Guidance 150(37)
6.1 The Restricted Two-Body Problem
150(4)
6.2 The Two-Dimensional Hit Equation
154(4)
6.3 In-Plane Error Analysis
158(5)
6.4 Three-Dimensional Error Analysis
163(7)
6.4.1 Actual Flight Time Approximation
166(1)
6.4.2 Down-Range Miss Distance, MDR
167(2)
6.4.3 Cross-Range Miss Distance, MCR
169(1)
6.5 Effects of the Earth's Rotation
170(4)
6.6 Effects of Earth's Oblateness and Geophysical Uncertainties
174(1)
6.6.1 Effects of Other Perturbations
175(1)
6.7 General Solution of Ballistic Guidance Problems
175(9)
6.7.1 General Framework
175(1)
6.7.2 Problem Formulation
176(1)
6.7.3 Examples
176(3)
6.7.4 Targeting
179(2)
6.7.5 Miss Analysis
181(3)
6.8 Summary of Key Results
184(1)
6.9 Bibliographic Notes for Further Reading
184(1)
6.10 Homework Problems
185(2)
7 Midcourse Guidance 187(12)
7.1 Velocity-to-Be-Gained Guidance
188(3)
7.1.1 Velocity-to-Be-Gained Guidance with Unlimited Thrust
189(1)
7.1.2 Velocity-to-Be-Gained Guidance with Limited Thrust
189(2)
7.2 Guidance by State Feedback
191(2)
7.3 Combined Navigation and Guidance
193(3)
7.4 Summary of Key Results
196(1)
7.5 Bibliographic Notes for Further Reading
197(1)
7.6 Homework Problems
197(2)
8 Optimization 199(23)
8.1 Unconstrained Optimization on Rn
200(5)
8.2 Constrained Optimization on Rn
205(9)
8.2.1 Lagrange Multipliers
206(2)
8.2.2 Second-Order Conditions
208(6)
8.3 Inequality Constraints on Rn
214(1)
8.4 Optimal Control of Discrete-Time Systems
215(2)
8.5 Summary of Key Results
217(1)
8.6 Bibliographic Notes for Further Reading
218(1)
8.7 Homework Problems
218(4)
9 Optimal Guidance 222(47)
9.1 Problem Formulation
222(3)
9.2 Examples
225(3)
9.3 Optimal Control without Control Constraints
228(5)
9.4 The Maximum Principle
233(13)
9.4.1 Greed
235(1)
9.4.2 The Transversality Conditions
236(1)
9.4.3 Target Sets
237(1)
9.4.4 Time-Optimal Control of Double Integrator
238(3)
9.4.5 Optimal Evasion through Jinking
241(5)
9.5 Dynamic Programming
246(13)
9.5.1 Motivational Example: Dynamic Programming
246(2)
9.5.2 The Principle of Optimality
248(1)
9.5.3 Backward Dynamic Programming
249(1)
9.5.4 Continuous-Time Dynamic Programming
250(2)
9.5.5 The Linear Quadratic Regulator
252(3)
9.5.6 The Linear Quadratic Gaussian Regulator
255(1)
9.5.7 Relationship between the Maximum Principle and Dynamic Programming
256(2)
9.5.8 The Hamilton—Jacobi—Bellman Equation
258(1)
9.5.9 Dynamic Programming Summary
258(1)
9.6 The Maximum Principle and Dynamic Programming
259(2)
9.7 Summary of Key Results
261(1)
9.8 Bibliographic Notes for Further Reading
262(1)
9.9 Homework Problems
262(7)
10 Introduction to Differential Games 269(19)
10.1 Taxonomy of Two-Player Games
269(3)
10.2 Example of a Simple Pursuit Game: Two-Player Football Scrimmage
272(3)
10.2.1 Modeling
272(1)
10.2.2 Analysis
272(1)
10.2.3 The Apollonius Circle Theorem
273(2)
10.2.4 Solution to the Football Two-Player Scrimmage Problem
275(1)
10.3 The Bellman—Isaacs Equation
275(1)
10.4 The Homicidal Chauffeur: Modeling
276(4)
10.5 The Homicidal Chauffeur: Features of the Solution
280(1)
10.6 A Game-Theoretic View of Proportional Navigation
281(4)
10.7 Summary of Key Results
285(1)
10.8 Bibliographic Notes for Further Reading
285(1)
10.9 Homework Problems
285(3)
Epilogue 288(7)
Appendix A: Useful Definitions and Mathematical Results 295(10)
A.1 Results from Topology
295(2)
A.2 Results from Linear Algebra
297(3)
A.3 Taylor's Theorem
300(1)
A.4 Newton's Method
301(1)
A.5 The Implicit Function Theorem
301(4)
Bibliography 305(4)
Index 309
Pierre T. Kabamba is a Professor of Aerospace Engineering at the University of Michigan. Professor Kabamba's research interests include linear and nonlinear dynamic systems, robust control, guidance and navigation, and intelligent control. His recent research activities are aimed at the development of a quasilinear control theory that is applicable to linear plants with nonlinear sensors or actuators. He has also done work in the design, scheduling, and operation of multi-spacecraft interferometric imaging systems used to obtain images of exosolar planets. Moreover, he has also done work in the analysis and optimization of random search algorithms. Finally, he is also doing work in simultaneous path planning and communication scheduling for unmanned aerial vehicles (UAVs) under the constraint of radar avoidance. He is author or co-author of more than 170 publications featured in refereed journals and conferences and of numerous book chapters. Professor Anouck Girard's interests include nonlinear control and systems engineering with applications in unmanned vehicle systems; hybrid, distributed, and embedded systems; maneuver coordination; and control of vehicles and mobile ground, air, and ocean robots. She is the director of the Aerospace Robotics and Control (ARC) Laboratory, in which cooperative control algorithms are developed and implemented in small and micro air vehicles and/or ground robots, and the principal investigator and director of the Michigan/AFRL Collaborative Center in Control Science (MACCCS) since 2007.
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