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Decentralized Estimation and Control for Multisensor Systems




ISBN 9780849318658
Published January 29, 1998 by CRC Press
248 Pages

 
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Book Description

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.
  • Table of Contents

    Introduction
    Background
    Motivation
    Problem Statement
    Approach
    Principal Contributions
    Book Outline
    Estimation and Information Space
    Introduction
    The Kalman Filter
    The Information Filter
    The Extended Kalman Filter (EKF)
    The Extended Information Filter (EIF)
    Examples of Estimation in Nonlinear Systems
    Summary
    Decentralized Estimation for Multisensor Systems
    Introduction
    Multisensor Systems
    Decentralized Systems
    Decentralized Estimators
    The Limitations of Fully Connected Decentralization
    Summary
    Scalable Decentralized Estimation
    Introduction
    An Extended Example
    The Moore-Penrose Generalized Inverse: T+
    Generalized Internodal Transformation
    Special Cases of Tji(k)
    Distributed and Decentralized Filters
    Summary
    Scalable Decentralized Control
    Introduction
    Optimal Stochastic Control
    Decentralized Multisensor Based Control
    Simulation Example
    Summary
    Multisensor Applications: A Wheeled Mobile Robot
    Introduction
    Wheeled Mobile Robot (WMR) Modeling
    Decentralized WMR Control
    Hardware Design and Construction
    Software Development
    On-Vehicle Software
    Summary
    Results and Performance Analysis
    Introduction
    System Performance Criteria
    Simulation Results
    WMR Experimental Results
    Summary
    Conclusions and Future Research
    Introduction
    Summary of Contributions
    Research Appraisal
    Future Research Directions
    Bibliography

    ...
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