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E-grāmata: Optimal Mobile Sensing and Actuation Policies in Cyber-physical Systems

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  • Formāts: PDF+DRM
  • Izdošanas datums: 14-Oct-2011
  • Izdevniecība: Springer London Ltd
  • Valoda: eng
  • ISBN-13: 9781447122623
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Optimal Mobile Sensing and Actuation Policies in Cyber-physical SystemsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 14-Oct-2011
  • Izdevniecība: Springer London Ltd
  • Valoda: eng
  • ISBN-13: 9781447122623
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A successful cyber-physical system, a complex interweaving of hardware and software with some part of the physical environment, depends on proper identification of the, often pre-existing, physical element. A bespoke "cyber" part of the system may then be designed from scratch. Optimal Mobile Sensing and Actuation Strategies in Cyber-physical Systems focuses on distributed-parameter systems the dynamics of which can be modelled with partial differential equations. These are very challenging to observe, their states and inputs being distributed throughout a spatial domain. Consequently, systematic approaches to the optimization of sensor location have to be devised for parameter estimation. The text begins by reviewing the field of cyber-physical systems and introducing background notions of distributed parameter systems and optimal observation theory. New research problems are then defined within this framework. Two important problems considered are optimal mobile sensor trajectory planning and the accuracy effects and allocation of remote sensors. These are followed up with a solution to the problem of optimal robust estimation. Actuation policies are then introduced into the framework with the purpose of improving estimation and optimizing the trajectories of both sensors and actuators simultaneously. The large number of illustrations within the text will assist the reader to visualize the application of the methods proposed. A group of similar examples are used throughout the book to help the reader assimilate the material more easily. The monograph concentrates on the use of methods for which a cyber-physical-systems infrastructure is required. The methods are computationally heavy and require mobile sensors and actuators with communications abilities. Application examples cover fields from environmental science to national security so that readers are encouraged to link the ideas of cyber-physical systems with their own research.

Incorporating a number of illustrations, this book focuses on distributed-parameter systems, the dynamics of which can be modelled with partial differential equations. Application examples cover fields ranging from environmental science to national security.
1 Introduction
1(16)
1.1 Background on Cyber-physical Systems and Distributed Parameter Systems
1(6)
1.1.1 Cyber-physical Systems
1(6)
1.1.2 Distributed Parameter Systems
7(1)
1.2 Motivations for Monograph Research and Application Scenarios
7(6)
1.2.1 Optimal Measurements in DPS
8(1)
1.2.2 Scenarios for Optimal Operations of a Mobile Actuator/Sensor Network
9(3)
1.2.3 Fractional-Order Cyber-physical Systems (FOCPS)
12(1)
1.3 Summary of Monograph Contributions
13(1)
1.4 Preview of
Chapters
14(3)
2 Distributed Parameter Systems: Controllability, Observability, and Identification
17(14)
2.1 Mathematical Description
17(4)
2.1.1 System Definition
17(1)
2.1.2 Actuator Definition
18(1)
2.1.3 Sensor Definition
19(2)
2.2 Regional Controllability
21(1)
2.3 Regional Observability
22(2)
2.4 Parameter Identification and Optimal Experiment Design
24(5)
2.4.1 System Definition
24(1)
2.4.2 Parameter Identification
25(1)
2.4.3 Sensor Location Problem
25(1)
2.4.4 Sensor Clustering Phenomenon
26(1)
2.4.5 Dependence of the Solution on Initial Parameter Estimates
27(2)
2.5
Chapter Summary
29(2)
3 Optimal Heterogeneous Mobile Sensing for Parameter Estimation of Distributed Parameter Systems
31(20)
3.1 Introduction
31(1)
3.2 Optimal Sensor Location Problem
32(3)
3.3 Mobile Sensor Model
35(2)
3.3.1 Node Dynamics
35(1)
3.3.2 Pathwise State Constraints
35(1)
3.3.3 Parameterization of Vehicle Controls
36(1)
3.4 Characterization of Optimal Solutions
37(3)
3.5 Optimal Control Formulation of the Search for the Candidate Support Point
40(1)
3.6 Illustrative Example
41(2)
3.7 Optimal Measurement Problem in the Average Sense
43(6)
3.7.1 A Limitation of the Design of Optimal Sensing Policies for Parameter Estimation
43(2)
3.7.2 Problem Definition
45(1)
3.7.3 An Illustrative Example
46(3)
3.8
Chapter Summary
49(2)
4 Optimal Mobile Remote Sensing Policies
51(12)
4.1 Introduction
51(3)
4.1.1 Literature Review
51(1)
4.1.2 Problem Formulation for PDE Parameter Estimation
52(2)
4.2 Optimal Measurement Problem
54(2)
4.2.1 Mobile Sensor Model
54(2)
4.2.2 Problem Definition
56(1)
4.3 Optimal Control Formulation
56(2)
4.4 An Illustrative Example
58(1)
4.5
Chapter Summary
59(4)
5 Online Optimal Mobile Sensing Policies: Finite-Horizon Control Framework
63(16)
5.1 Introduction
63(1)
5.2 Optimal Mobile Sensing Policy: Finite-Horizon Closed-Loop Solution
64(7)
5.2.1 A DPS and Its Mobile Sensors
64(1)
5.2.2 Interlaced Optimal Trajectory Planning
65(2)
5.2.3 Illustrative Simulations
67(1)
5.2.4 A Second Illustrative Example
67(4)
5.3 Communication Topology in Online Optimal Sensing Policy for Parameter Estimation of Distributed Parameter Systems
71(4)
5.3.1 The Interlaced Scheme with Communication Topology
71(1)
5.3.2 An Illustrative Example
72(3)
5.4 Convergence of the Interlaced Scheme
75(2)
5.5
Chapter Summary
77(2)
6 Optimal Mobile Actuation/Sensing Policies for Parameter Estimation of Distributed Parameter Systems
79(18)
6.1 Introduction
79(2)
6.1.1 Problem Formulation for PDE Parameter Estimation
80(1)
6.2 Optimal Actuation Problem
81(4)
6.2.1 Mobile Actuator Model
81(1)
6.2.2 Problem Definition
82(1)
6.2.3 An Illustrative Example
83(2)
6.3 Optimal Measurement/Actuation Problem
85(9)
6.3.1 Mobile Sensor/Actuator Model
85(6)
6.3.2 Problem Definition
91(1)
6.3.3 An Illustrative Example
92(2)
6.4
Chapter Summary
94(3)
7 Optimal Mobile Sensing with Fractional Sensor Dynamics
97(20)
7.1 Introduction
97(1)
7.2 Fractional Optimal Control Problem Formulation
98(1)
7.3 Oustaloup Recursive Approximation of the Fractional Derivative Operator
99(2)
7.4 Fractional Optimal Control Problem Reformulation. I
101(1)
7.5 Impulse-Response-Based Linear Approximation of Fractional Transfer Functions
102(3)
7.5.1 Approximation Method
102(2)
7.5.2 Suboptimal Approximation of the Fractional Integrator
104(1)
7.6 Fractional Optimal Control Problem Reformulation. II
105(2)
7.7 Illustrative Examples
107(3)
7.7.1 A Linear Time-Invariant Problem
107(1)
7.7.2 A Linear Time-Variant Problem
108(2)
7.8 Optimal Mobile Sensing Policies with Fractional Sensor Dynamics
110(5)
7.8.1 Sensor Dynamics
110(1)
7.8.2 Optimal Measurement Problem
111(1)
7.8.3 Optimal Control Problem Reformulation
112(1)
7.8.4 An Illustrative Example
113(2)
7.9
Chapter Summary
115(2)
8 Optimal Mobile Remote Sensing Policy for Downscaling and Assimilation Problems
117(18)
8.1 Background on Downscaling and Data Assimilation
117(7)
8.1.1 Downscaling
117(3)
8.1.2 Data Assimilation
120(4)
8.2 Downscaling and Assimilation Problems for Surface Soil Moisture
124(4)
8.2.1 Introduction
124(1)
8.2.2 Kaheil and McKee's Algorithm
124(4)
8.3 Introduction of UAV-Based Remote Sensors
128(1)
8.4 Optimal Trajectories for Data Assimilation
128(3)
8.4.1 Description of the Problem
128(2)
8.4.2 Problem Formulation
130(1)
8.4.3 Numerical Method to Find the Solution
130(1)
8.5 An Illustrative Example
131(2)
8.5.1 System's Description
131(1)
8.5.2 Results
131(2)
8.6
Chapter Summary
133(2)
9 Conclusions and Future Work
135(4)
9.1 Conclusions
135(1)
9.2 Future Research Directions
136(3)
9.2.1 Communication Topology Influence on Regional Controllability and Observability for DPS
137(1)
9.2.2 Directed Communication Topologies
137(1)
9.2.3 Regional Identifiability of a DPS
137(2)
Appendix A Notation
139(4)
A.1 General Notation
139(1)
A.2 Special Notation in Chap. 2
140(1)
A.3 Special Notation in Chap. 3
140(1)
A.4 Special Notation in Chap. 4
140(1)
A.5 Special Notation in Chap. 6
140(1)
A.6 Special Notation in Chap. 7
141(2)
Appendix B RIOTS Tutorial
143(4)
B.1 Introduction
143(1)
B.2 Features of RIOTS_95
144(1)
B.3 Class of Optimal Control Problems Solvable by RIOTS_95
145(2)
Appendix C Implementations
147(14)
C.1 Remote Sensors Trajectory Optimization
147(5)
C.2 Online Scheme for Trajectory Optimization
152(5)
C.3 Fractional-Order Trajectory Optimization
157(4)
References 161(8)
Index 169
Dr. YangQuan Chen has authored over 200 academic papers plus numerous technical reports. He co-authored two textbooks:"System Simulation Techniques with MATLAB®/Simulink" (with Dingyu Xue. Tsinghua University Press, April02, ISBN 7-302-05341-3/TP3137, in Chinese) and "Solving Advanced Applied Mathematical Problems Using Matlab" (with Dingyu Xue. Tsinghua University Press. August04. 419 pages in Chinese, ISBN 7-302-09311-3/O.392); and five research monographs: "Plastic Belt for Projectiles" (with Y. Shi. Shaanxi Science and Technology Press, Jan. 1995, ISBN 7-5369-2277-9/TJ.1, in Chinese), "Iterative Learning Control" (with C. Wen. LNCIS, Springer-Verlag, Nov. 1999, ISBN: 1-85233-190-9), Iterative Learning Control (with Hyo-Sung Ahn and Kevin L. Moore. Springer, July07, ISBN: 978-1-84628-846-3), Optimal Observation for Cyber-physical Systems(with Zhen Song, Chellury Sastry and Nazif Tas. Springer, July09, ISBN: 978-1-84882-655-7), and Fractional-order Systems and Controls (with Concepción A. Monje, Blas M. Vinagre, Dingyu Xue and Vicente Feliu, ISBN:978-1-84996-334-3). His current research interests include autonomous navigation and intelligent control of a team of unmanned ground vehicles, machine vision for control and automation, distributed control systems (MAS-net: mobile actuator-sensor networks), fractional-order control, interval computation, and iterative/repetitive/adaptive learning control. Currently, he serves as an Associate Editor for IEEE CSSCEB. He was also an Associate Editor of ISA Review Board for AACC's American Control Conference (ACC05). He has been the Co-Organizer and Instructor of the Tutorial Workshops on Fractional-order Calculus in Control and Robotics at IEEE 2002 Conference on Decision and Control (CDC02), and Applied Fractional Calculus in Controls and Signal Processing at CDC10 and a founding member of the ASME subcommittee on Fractional Dynamics.
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