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DECENTRALIZED ESTIMATION AND CONTROL FOR MULTISENSOR SYSTEMS

DECENTRALIZED ESTIMATION AND CONTROL FOR MULTISENSOR SYSTEMS

DECENTRALIZED ESTIMATION AND CONTROL FOR MULTISENSOR SYSTEMS
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DECENTRALIZED ESTIMATION AND CONTROL FOR MULTISENSOR SYSTEMS Hardback - 1998

by ARTHUR G.O. MUTAMBARA ,

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CR Press, 1998. 1st. Hardcover. New/New.
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Details

  • Title DECENTRALIZED ESTIMATION AND CONTROL FOR MULTISENSOR SYSTEMS
  • Author ARTHUR G.O. MUTAMBARA ,
  • Binding Hardback
  • Edition 1st
  • Condition New
  • Pages 248
  • Volumes 1
  • Language ENG
  • Publisher CR Press, Boca Raton, Florida, U.S.A.
  • Publication date 1998
  • Features Bibliography
  • Bookseller's Inventory # AME_9780849318658
  • ISBN 9780849318658 / 0849318653
  • Weight 1.13 lbs (0.51 kg)
  • Dimensions 9.57 x 6.4 x 0.77 in (24.31 x 16.26 x 1.96 cm)
  • Category Technology & Industrial Arts
  • Library of Congress subjects Robots - Control systems, Automatic control
  • Library of Congress Catalogue Number 97051553
  • Dewey Decimal Code 629.8
  • Quantity available 5

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From the publisher

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.

    • About the author

    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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