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El. knyga: Decentralized Estimation and Control for Multisensor Systems

  • Formatas: 256 pages
  • Išleidimo metai: 20-May-2019
  • Leidėjas: CRC Press Inc
  • Kalba: eng
  • ISBN-13: 9781351456500
Kitos knygos pagal šią temą:
Decentralized Estimation and Control for Multisensor SystemsDidesnis paveikslėlis
  • Formatas: 256 pages
  • Išleidimo metai: 20-May-2019
  • Leidėjas: CRC Press Inc
  • Kalba: eng
  • ISBN-13: 9781351456500
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Explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems that would have applications in modular robotics and complex or large-scale systems such as the Mars Rover, the Mir station, and Space Shuttle Columbia. Unlike the hierarchical or centralized structure for gathering and processing data used by most existing algorithms, all information is processed locally in a fully decentralized system. The algorithms developed so far for decentralized data fusion, based on the linear information filter, obtain the same results as conventional centralized systems, but have limited scalability and are wasteful of communications and computational resources. Annotation c. by Book News, Inc., Portland, Or.

Decentralized Estimation and Control for
Multisensor Systems explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. Such algorithms have extensive applications in modular robotics and complex or large scale systems, including the Mars Rover, the Mir station, and Space Shuttle Columbia.

Most existing algorithms use some form of hierarchical or centralized structure for data gathering and processing. In contrast, in a fully decentralized system, all information is processed locally. A decentralized data fusion system includes a network of sensor nodes - each with its own processing facility, which together do not require any central processing or central communication facility. Only node-to-node communication and local system knowledge are permitted.

Algorithms for decentralized data fusion systems based on the linear information filter have been developed, obtaining decentrally the same results as those in a conventional centralized data fusion system. However, these algorithms are limited, indicating that existing decentralized data fusion algorithms have limited scalability and are wasteful of communications and computation resources.

Decentralized Estimation and Control for
Multisensor Systems aims to remove current limitations in decentralized data fusion algorithms and to extend the decentralized principle to problems involving local control and actuation.
The text discusses:
  • Generalizing the linear Information filter to the problem of estimation for nonlinear systems
  • Developing a decentralized form of the algorithm
  • Solving the problem of fully connected topologies by using generalized model distribution where the nodal system involves only locally relevant states
  • Reducing computational requirements by using smaller local model sizes
  • Defining internodal communication
  • Developing estimation algorithms for different models
  • Applying the decentralized algorithms to the problem of decentralized control
  • Demonstrating the theory to a modular wheeled mobile robot, a vehicle system with nonlinear kinematics and distributed means of acquiring information
  • Extending the applications to other robotic systems and large scale systems

    Decentralized Estimation and Control for
    Multisensor Systems addresses how decentralized estimation and control systems are rapidly becoming indispensable tools in a diverse range of applications - such as process control systems, aerospace, and mobile robotics - providing a self-contained, dynamic resource concerning electrical and mechanical engineering.
    • 1 Introduction
    		1(18)
    1.1 Background
    		1(2)
    1.2 Motivation
    		3(9)
    1.2.1 Modular Robotics
    		4(1)
    1.2.2 The Mars Sojourner Rover
    		4(2)
    1.2.3 The MIT Humanoid Robot (Cog)
    		6(1)
    1.2.4 Large Scale Systems
    		7(1)
    1.2.5 The Russian Mir Space Station
    		7(2)
    1.2.6 The Space Shuttle Columbia
    		9(3)
    1.3 Problem Statement
    		12(1)
    1.4 Approach
    		13(1)
    1.4.1 Estimation
    		13(1)
    1.4.2 Control
    		13(1)
    1.4.3 Applications
    		13(1)
    1.5 Principal Contributions
    		14(1)
    1.6 Book Outline
    		15(4)
    2 Estimation and Information Space
    		19(36)
    2.1 Introduction
    		19(1)
    2.2 The Kalman Filter
    		19(3)
    2.2.1 System Description
    		20(1)
    2.2.2 Kalman Filter Algorithm
    		21(1)
    2.3 The Information Filter
    		22(11)
    2.3.1 Information Space
    		22(4)
    2.3.2 Information Filter Derivation
    		26(2)
    2.3.3 Filter Characteristics
    		28(1)
    2.3.4 An Example of Linear Estimation
    		28(3)
    2.3.5 Comparison of the Kalman and Information Filters
    		31(2)
    2.4 The Extended Kalman Filter (EKF)
    		33(6)
    2.4.1 Nonlinear State Space
    		34(1)
    2.4.2 EKF Derivation
    		34(4)
    2.4.3 Summary of the EKF Algorithm
    		38(1)
    2.5 The Extended Information Filter (EIF)
    		39(5)
    2.5.1 Nonlinear Information Space
    		39(1)
    2.5.2 EIF Derivation
    		40(3)
    2.5.3 Summary of the EIF Algorithm
    		43(1)
    2.5.4 Filter Characteristics
    		43(1)
    2.6 Examples of Estimation in Nonlinear Systems
    		44(9)
    2.6.1 Nonlinear State Evolution and Linear Observations
    		44(2)
    2.6.2 Linear State Evolution with Nonlinear Observations
    		46(2)
    2.6.3 Nonlinear State Evolution with Nonlinear Observations
    		48(3)
    2.6.4 Comparison of the EKF and EIF
    		51(2)
    2.7 Summary
    		53(2)
    3 Decentralized Estimation for Multisensor Systems
    		55(26)
    3.1 Introduction
    		55(1)
    3.2 Multisensor Systems
    		56(8)
    3.2.1 Sensor Classification and Selection
    		56(3)
    3.2.2 Positions of Sensors in a Data Acquisition System
    		59(1)
    3.2.3 The Advantages of Multisensor Systems
    		60(1)
    3.2.4 Data Fusion Methods
    		61(1)
    3.2.5 Fusion Architectures
    		62(2)
    3.3 Decentralized Systems
    		64(4)
    3.3.1 The Case for Decentralization
    		64(2)
    3.3.2 Survey of Decentralized Systems
    		66(2)
    3.4 Decentralized Estimators
    		68(9)
    3.4.1 Decentralizing the Observer
    		68(1)
    3.4.2 The Decentralized Information Filter (DIF)
    		69(3)
    3.4.3 The Decentralized Kalman Filter (DKF)
    		72(2)
    3.4.4 The Decentralized Extended Information Filter (DEIF)
    		74(2)
    3.4.5 The Decentralized Extended Kalman Filter (DEKF)
    		76(1)
    3.5 The Limitations of Fully Connected Decentralization
    		77(2)
    3.6 Summary
    		79(2)
    4 Scalable Decentralized Estimation
    		81(40)
    4.1 Introduction
    		81(2)
    4.1.1 Model Distribution
    		82(1)
    4.1.2 Nodal Transformation Determination
    		82(1)
    4.2 An Extended Example
    		83(13)
    4.2.1 Unscaled Individual States
    		83(3)
    4.2.2 Proportionally Dependent States
    		86(2)
    4.2.3 Linear Combination of States
    		88(5)
    4.2.4 Generalizing the Concept
    		93(1)
    4.2.5 Choice of Transformation Matrices
    		94(1)
    4.2.6 Distribution of Models
    		94(2)
    4.3 The Moore-Penrose Generalized Inverse: T(+)
    		96(5)
    4.3.1 Properties and Theorems of T(+)
    		97(3)
    4.3.2 Computation of T(+)
    		100(1)
    4.4 Generalized Internodal Transformation
    		101(7)
    4.4.1 State Space Internodal Transformation: V(ji)(k)
    		101(5)
    4.4.2 Information Space Internodal Transformation: T(ji)(k)
    		106(2)
    4.5 Special Cases of T(ji)(k)
    		108(4)
    4.5.1 Scaled Orthonormal T(i)(k) and T(j)(k)
    		108(1)
    4.5.2 Diagonal I(+)(j)(z(j)(k))
    		109(1)
    4.5.3 Nonsingular and Diagonal I(+)(j)(Z(j)(k))
    		109(1)
    4.5.4 Row Orthonormal C(j)(k) and Nonsingular R(j)(k)
    		110(1)
    4.5.5 Row Orthonormal T(i)(k) and T(j)(k)
    		111(1)
    4.5.6 Reconstruction of Global Variables
    		111(1)
    4.6 Distributed and Decentralized Filters
    		112(7)
    4.6.1 The Distributed and Decentralized Kalman Filter (DDKF)
    		112(2)
    4.6.2 The Distributed and Decentralized Information Filter (DDIF)
    		114(2)
    4.6.3 The Distributed and Decentralized Extended Kalman Filter (DDEKF)
    		116(1)
    4.6.4 The Distributed and Decentralized Extended Information Filter (DDEIF)
    		117(2)
    4.7 Summary
    		119(2)
    5 Scalable Decentralized Control
    		121(20)
    5.1 Introduction
    		121(1)
    5.2 Optimal Stochastic Control
    		121(7)
    5.2.1 Stochastic Control Problem
    		122(1)
    5.2.2 Optimal Stochastic Solution
    		123(3)
    5.2.3 Nonlinear Stochastic Control
    		126(1)
    5.2.4 Centralized Control
    		127(1)
    5.3 Decentralized Multisensor Based Control
    		128(7)
    5.3.1 Fully Connected Decentralized Control
    		129(2)
    5.3.2 Distribution of Control Models
    		131(1)
    5.3.3 Distributed and Decentralized Control
    		132(2)
    5.3.4 System Characteristics
    		134(1)
    5.4 Simulation Example
    		135(5)
    5.4.1 Continuous Time Models
    		135(2)
    5.4.2 Discrete Time Global Models
    		137(1)
    5.4.3 Nodal Transformation Matrices
    		138(1)
    5.4.4 Local Discrete Time Models
    		139(1)
    5.5 Summary
    		140(1)
    6 Multisensor Applications: A Wheeled Mobile Robot
    		141(42)
    6.1 Introduction
    		141(1)
    6.2 Wheeled Mobile Robot (WMR) Modeling
    		142(7)
    6.2.1 Plane Motion Kinematics
    		143(2)
    6.2.2 Decentralized Kinematics
    		145(4)
    6.3 Decentralized WMR Control
    		149(11)
    6.3.1 General WMR System Models
    		150(2)
    6.3.2 Specific WMR Implementation Models
    		152(6)
    6.3.3 Driven and Steered Unit (DSU) Control
    		158(1)
    6.3.4 Application of Internodal Transformation
    		159(1)
    6.4 Hardware Design and Construction
    		160(7)
    6.4.1 WMR Modules
    		161(2)
    6.4.2 A Complete Modular Vehicle
    		163(2)
    6.4.3 Transputer Architecture
    		165(2)
    6.5 Software Development
    		167(7)
    6.5.1 Nodal Program (Communicating Control Process)
    		168(5)
    6.5.2 Configuration Program (Decentralized Control)
    		173(1)
    6.6 On-Vehicle Software
    		174(7)
    6.6.1 Nodal Software
    		174(2)
    6.6.2 Decentralized Motor Control
    		176(1)
    6.6.3 WMR Trajectory Generation
    		176(5)
    6.7 Summary
    		181(2)
    7 Results and Performance Analysis
    		183(26)
    7.1 Introduction
    		183(1)
    7.2 System Performance Criteria
    		183(3)
    7.2.1 Estimation Criteria
    		184(1)
    7.2.2 Control Criteria
    		185(1)
    7.3 Simulation Results
    		186(3)
    7.3.1 Innovations
    		186(1)
    7.3.2 State Estimates
    		187(1)
    7.3.3 Information Estimates and Control
    		188(1)
    7.4 WMR Experimental Results
    		189(15)
    7.4.1 Trajectory Tracking
    		190(2)
    7.4.2 Innovations and Estimated Control Errors
    		192(12)
    7.5 Discussion of Results
    		204(3)
    7.5.1 Local DSU Innovations
    		204(2)
    7.5.2 Wheel Estimated Control Errors
    		206(1)
    7.5.3 WMR Body Estimates
    		206(1)
    7.6 Summary
    		207(2)
    8 Conclusions and Future Research
    		209(8)
    8.1 Introduction
    		209(1)
    8.2 Summary of Contributions
    		209(2)
    8.2.1 Decentralized Estimation
    		210(1)
    8.2.2 Decentralized Control
    		210(1)
    8.2.3 Applications
    		210(1)
    8.3 Research Appraisal
    		211(2)
    8.3.1 Decentralized Estimation
    		211(2)
    8.3.2 Decentralized Control
    		213(1)
    8.4 Future Research Directions
    		213(4)
    8.4.1 Theory
    		214(1)
    8.4.2 Applications
    		215(2)
    Bibliography 217(10)
    Index 227
    Professor Arthur G.O. Mutambara- Arthur Mutambara is a robotics scientist, professor, and former Deputy Prime Minister of Zimbabwe. He is the Managing Director and CEO of the Africa Technology & Business Institute. Main research focus: wheeled mobile robots, decentralized communication in scalable flight formation, mechatronic design methodology, and modular robots.
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