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E-raamat: Predictive Filtering for Microsatellite Control System

(Academician, International Academy of Astronautics (IAA)), (Associate Professor, School of Automation, Northwestern Polytechnical University, China), (Associate Research Fellow)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 26-Nov-2020
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128218662
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Predictive Filtering for Microsatellite Control SystemSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 26-Nov-2020
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128218662
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Predictive Filtering for Microsatellite Control Systems introduces technological design, modeling, stability analysis, predictive filtering, state estimation problem and real-time operation of spacecraft control systems in aerospace engineering. The book gives a systematically and almost self-contained description of the many facets of envisaging, designing, implementing or experimentally exploring predictive filtering for spacecraft control systems, along with the adequate designs of integrated modeling, dynamics, state estimation, and signal processing of spacecrafts and nonlinear systems.

  • Unifies existing and emerging concepts concerning predictive filtering theory, state estimation, and signal processing for spacecraft control systems
  • Provides a series of latest results in, including but not limited to, nonlinear filtering, attitude determination, and state estimation towards spacecraft control systems
  • Gives numerical and simulation results in each chapter in order to reflect the engineering practice and demonstrate the main focus of the developed analysis and synthesis approach
  • Covers advanced topics in nonlinear filtering with aerospace application
List of figures
xiii
List of tables
xvii
List of algorithms
xix
Biography xxi
Preface xxiii
Acknowledgments xxv
Symbols and abbreviations xxvii
1 Overview
1.1 Introduction
1(1)
1.2 Microsatellite control system and its design
2(2)
1.2.1 Microsatellite control system
2(1)
1.2.2 Microsatellite control system design
3(1)
1.3 Attitude and orbit determination subsystem design
4(5)
1.3.1 Sensors for microsatellite control system
5(1)
1.3.2 State estimation method design
6(3)
1.4 Review of nonlinear filtering
9(6)
1.4.1 Nonlinear Kalman filtering
9(2)
1.4.2 Predictive filtering
11(1)
1.4.3 Particle Filtering
12(1)
1.4.4 Robust filtering
13(1)
1.4.5 Nonlinear filters applied to satellite control system
14(1)
1.5 Motivations for predictive filtering for microsatellite control systems
15(1)
1.6 What is in this book
16(5)
Part I Preliminaries
2 Fundamental of predictive filtering
2.1 Introduction
21(1)
2.2 Basic probability of random vector
21(4)
2.2.1 Random vector
22(1)
2.2.2 Mean vector
23(1)
2.2.3 Covariance matrix
23(1)
2.2.4 Normal distribution random vector
24(1)
2.2.5 White noise process
24(1)
2.3 Preliminary definitions and lemma
25(1)
2.4 Desired properties of filter
26(1)
2.5 Basic theory of predictive filtering
27(7)
2.5.1 Derivation of predictive filter
27(2)
2.5.2 Estimation performance of predictive filter
29(5)
2.6 Summary
34(1)
3 Modeling of microsatellite control system
3.1 Introduction
35(1)
3.2 Definition of coordinate frames
36(1)
3.3 Modeling of single microsatellite control system
36(2)
3.3.1 Mathematical model of attitude control system
37(1)
3.3.2 Mathematical model of orbit control system
38(1)
3.4 Modeling of microsatellite formation flying control system
38(27)
3.4.1 Definition of dimensionless expressions
39(1)
3.4.2 Assumption of small eccentricity
40(1)
3.4.3 And atmospheric drag perturbations
41(3)
3.4.4 Orbit propagation of the chief microsatellite
44(5)
3.4.5 Effect of perturbation forces on relative motion
49(7)
3.4.6 System equation of relative motion
56(8)
3.4.7 Modeling of microsatellite formation flying control system
64(1)
3.5 Modeling of distributed microsatellite attitude control system
65(1)
3.6 Summary
66(3)
Part II Sigma-point predictive filtering for microsatellite control system
4 Unscented predictive filter
4.1 Introduction
69(1)
4.2 Unscented predictive filter
70(1)
4.3 Estimation performance of UPF
71(11)
4.3.1 Estimation accuracy of modeling error
72(3)
4.3.2 Estimation accuracy of system states
75(7)
4.4 Covariance constraint analysis
82(2)
4.5 Stochastic stability of UPF
84(7)
4.5.1 Boundedness analysis of state estimation covariance
84(1)
4.5.2 Boundedness analysis of state estimation errors
84(7)
4.6 Application to microsatellite attitude control system
91(7)
4.6.1 Measurement model of star sensors
91(1)
4.6.2 Simulation results
92(6)
4.7 Application to microsatellite formation flying control system
98(12)
4.7.1 Measurement model of PSD
99(1)
4.7.2 Transformed microsatellite formation flying control system
100(1)
4.7.3 Implementation of UPF in formation flying control system
101(3)
4.7.4 Simulation results
104(6)
4.8 Summary
110(3)
5 Central difference predictive filter
5.1 Introduction
113(1)
5.2 Central difference predictive filter
113(2)
5.3 Estimation performance of CDPF
115(9)
5.3.1 Estimation accuracy of modeling error
115(6)
5.3.2 Estimation accuracy of system states
121(3)
5.4 Stochastic stability of CDPF
124(7)
5.4.1 Boundedness analysis of state estimation covariance
124(1)
5.4.2 Boundedness analysis of state estimation error
125(6)
5.5 Application to microsatellite attitude system with low precision sensors
131(8)
5.5.1 Measurement model of magnetometer and sun sensor
131(1)
5.5.2 Implementation of CDPF for attitude determination
131(1)
5.5.3 Simulation results
132(7)
5.6 Summary
139(2)
6 Cubature predictive filter
6.1 Introduction
141(1)
6.2 Third-degree spherical-radial cubature rule
142(1)
6.3 Cubature predictive filtering
143(1)
6.4 Estimation performance of CPF
143(10)
6.4.1 Estimation accuracy of modeling error
143(4)
6.4.2 Estimation accuracy of system state
147(6)
6.5 Stochastic stability of CPF
153(1)
6.5.1 Boundedness analysis of state estimation covariance
153(1)
6.5.2 Boundedness analysis of state estimation error
154(2)
6.6 Application to microsatellite attitude control system
156(3)
6.7 Application to microsatellite formation flying control system
159(5)
6.8 Summary
164(3)
Part III Predictive variable structure filtering for microsatellite control system
7 Predictive variable structure filter
7.1 Introduction
167(1)
7.2 Predictive variable structure filter design
168(1)
7.3 Theoretical basis of PVSF
169(1)
7.4 Stability of state estimation error
170(2)
7.5 Practical implementation of PVSF
172(3)
7.5.1 Eliminating the effect of sampling time
172(2)
7.5.2 Reducing chattering
174(1)
7.6 State estimation error covariance of PSVSF
175(4)
7.7 Application to microsatellite distributed attitude control system
179(5)
7.7.1 Measurement model of CCD camera
179(1)
7.7.2 Implementing PSVSF for distributed attitude control system
180(1)
7.7.3 Simulation results
181(3)
7.8 Application to microsatellite formation flying control system
184(3)
7.9 Summary
187(2)
8 Predictive adaptive variable structure filter
8.1 Introduction
189(1)
8.2 Predictive adaptive variable structure filter
189(4)
8.3 Derivation of sigma point PAVSF
193(1)
8.3.1 Unscented PAVSF
194(1)
8.3.2 Central difference PAVSF
194(1)
8.4 Strong tracking PAVSF
194(8)
8.4.1 Sequence orthogonal principle
197(1)
8.4.2 Derivation of strong tracking PAVSF
197(4)
8.4.3 Derivation of strong tracking sigma point PAVSF
201(1)
8.5 Application to microsatellite attitude control system
202(7)
8.5.1 Application to attitude control system with low precision sensors
202(4)
8.5.2 Application to distributed attitude control system
206(3)
8.6 Summary
209(2)
9 Predictive high-order variable structure filter
9.1 Introduction
211(1)
9.2 Predictive high-order variable structure filter
211(3)
9.3 Derivation of orthogonal PHVSF
214(3)
9.4 Huber PHVSF design
217(2)
9.5 Application to attitude control system with low precision sensors
219(5)
9.5.1 Simulation result in the presence of Gaussian white noise
221(2)
9.5.2 Simulation result in the presence of heavy-tailed noise
223(1)
9.6 Application to distributed attitude control system
224(5)
9.6.1 Simulation results
225(1)
9.6.2 Quantitative analysis
226(3)
9.7 Summary
229(4)
10 Conclusion and future work
10.1 General conclusion
233(1)
10.2 Future work
234(1)
References 235(12)
Index 247
Dr. Lu Cao is an associate research fellow. He is one of the world leading scholars in the field of nonlinear filtering/state estimation for spacecraft control systems. He is a productive researcher by publishing 50+ SCI journal papers, and has been authorized with 7 invention patents.

Dr. Cao has been the pioneer to establish advanced filtering algorithms in practice such as satellites and other. He designed the attitude determination and control system of Tian-Tuo (TT) series micro/nano satellites (totally 8 satellites), and has rich theoretical research and engineering application experience. As recognition for his significant contributions, he is a review expert in aerospace major journals such as Acta Astronautica, Advances in Space Research, Nonlinear Dynamics, and Aerospace China. Moreover, he has won many major awards, which include: the Excellent Masters Thesis, the Excellent Doctorate Thesis, and has been selected inYong Talent Lift Project”. Prof. Xiaoqian Chen is one of the world leading scholars in the field of spacecraft design. Prof. Chen took in charge of developing Tian-Tuo (TT) series micro/nano satellites (totally 8 satellites). Under his leadership, his team accomplished Tian-Yuan 1 on-orbit refueling satellite flight test, which is the second time in the world after the United States. He established advanced overall design theory of flight vehicles featuring multi-disciplinary design optimization (MDO).

He has been elected as an academician in International Academy of Astronautics (IAA), a member in International Astronautical Federation (IAF), a member in the robotics committee of Chinese Society of Astronautics, and an editorial board member in some major journals. He has won many major awards, which include: the Qiu Shi Outstanding Youth Prize for Practical Engineering”, Science & Technology Award for Chinese Youth”, Science & Technology Award for Chinese Youth by Chinese Society of Aeronautics and Astronautics (CSAA)”. Bing Xiao is an Associate Professor in the School of Automation, at Northwestern Polytechnical University, in China. His work focuses on spacecraft fault tolerant systems design. He has been elected as a senior member of the Chinese Youth Automation Committee, a platform for leading automation researchers in China. He has published over 70 papers.
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