
Cooperative Control and Optimization
Kluwer Academic Publishers
Published on 31. May 2002
Book
Hardback
XII, 308 pages
978-1-4020-0549-7 (ISBN)
Description
A cooperative system is defined to be multiple dynamic entities that share information or tasks to accomplish a common, though perhaps not singular, objective. Examples of cooperative control systems might include: robots operating within a manufacturing cell, unmanned aircraft in search and rescue operations or military surveillance and attack missions, arrays of micro satellites that form a distributed large aperture radar, employees operating within an organization, and software agents. The term entity is most often associated with vehicles capable of physical motion such as robots, automobiles, ships, and aircraft, but the definition extends to any entity concept that exhibits a time dependent behavior. Critical to cooperation is communication, which may be accomplished through active message passing or by passive observation. It is assumed that cooperation is being used to accomplish some common purpose that is greater than the purpose of each individual, but we recognize that the individual may have other objectives as well, perhaps due to being a member of other caucuses. This implies that cooperation may assume hierarchical forms as well. The decision-making processes (control) are typically thought to be distributed or decentralized to some degree. For if not, a cooperative system could always be modeled as a single entity. The level of cooperation may be indicated by the amount of information exchanged between entities. Cooperative systems may involve task sharing and can consist of heterogeneous entities. Mixed initiative systems are particularly interesting heterogeneous systems since they are composed of humans and machines. Finally, one is often interested in how cooperative systems perform under noisy or adversary conditions.
In December 2000, the Air Force Research Laboratory and the University of Florida successfully hosted the first Workshop on Cooperative Control and Optimization in Gainesville, Florida. This bookcontains selected refereed papers summarizing the participants' research in control and optimization of cooperative systems.
Audience: Faculty, graduate students, and researchers in optimization and control, computer sciences and engineering.
In December 2000, the Air Force Research Laboratory and the University of Florida successfully hosted the first Workshop on Cooperative Control and Optimization in Gainesville, Florida. This bookcontains selected refereed papers summarizing the participants' research in control and optimization of cooperative systems.
Audience: Faculty, graduate students, and researchers in optimization and control, computer sciences and engineering.
More details
Series
Edition
2002 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XII, 308 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 23 mm
Weight
653 gr
ISBN-13
978-1-4020-0549-7 (9781402005497)
DOI
10.1007/b130435
Schweitzer Classification
Other editions
Additional editions

Robert Murphey | Panos M. Pardalos
Cooperative Control and Optimization
Book
12/2010
Springer
€106.99
Shipment within 15-20 days
Content
Cooperative Control for Target Classification.- Guillotine Cut in Approximation Algorithms.- Unmanned Aerial Vehicles: Autonomous Control Challenges, A Researcher's Perspective.- Using Grasp for Choosing Best Periodic Observation Strategy in Stochastic Systems Filtering.- Cooperative Control of Robot Formations.- Cooperative Behavior Schemes for Improving The Effectiveness of Autonomous Wide Area Search Munitions.- On A General Framework To Study Cooperative Systems.- Cooperative Multi-agent Constellation Formation Under Sensing and Communication Constraints.- An Introduction to Collective and Cooperative Systems.- Cooperative Aircraft Control for Minimum Radar Exposure.- Robust Recursive Bayesian Estimation and Quantum Minimax Strategies.- Cooperative Control for Autonomous Air Vehicles.- Optimal Risk Path Algorithms.