Cooperative control and optimization
By: edited by Robert Murphey and Panos M. Pardalos.
Contributors: Murphey, Robert.; Pardalos, P. M. 1954-
Series: Applied optimization -- v. 66
Publisher: Kluwer Academic Publishers; Springer
Published: 2002
Place of publication: Dordrecht; Boston
Language: eng
Number of pages: 303
Subjects: Decision making.; Mathematical optimization.; Control theory.
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 book contains 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.
Notes: Includes bibliographical references.
Table of contents: title: Machine generated contents note: 1; title: Cooperative Control for Target Classification; title: P. R. Chandler, M. Pachter, Kendall E. Nygard and Dharba Swaroop; title: 1.1 Introduction; title: 1.2 Joint classification; title: 1.3 Assignment; title: 1.4 Hierarchical architecture; title: 1.5 Simulation; title: 1.6 Classification issues; title: 1.7 Conclusions; title: References; title: 2; title: Guillotine Cut in Approximation Algorithms; title: Xiuzhen Cheng, Ding-Zhu Du, Joon-Mo Kim and Hung Quang Ngo; title: 2.1 Introduction; title: 2.2 Rectangular partition and guillotine cut; title: 2.3 1-Guillotine cut; title: 2.4 m-Guillotine cut; title: 2.5 Portals; title: References; title: 3; title: Unmanned Aerial Vehicles: Autonomous Control Challenges, A Re-; title: searcher's Perspective; title: Bruce T. Clough; title: 3.1 Introduction; title: 3.2 Background; title: 3.3 The Challenges; title: 3.4 How are we approaching these challenges?; title: 3.5 Where are we heading from here?; title: References; title: Appendix: Notes; title: 4; title: Optimal periodic stochastic filtering with GRASP; title: Paola Festa and Giancarlo Raiconi; title: 4.1 Introduction and problem statement; title: 4.2 Two interesting particular cases; title: 4.3 Discrete problem formulation; title: 4.4 Numerical results; title: References; title: 5; title: Cooperative Control of Robot Formations; title: Rafael Fierro, Peng Song, Aveek Das, and Vijay Kumar; title: 5.1 Introduction; title: 5.2 Framework for cooperative control; title: 5.3 Formation control; title: 5.4 Trajectory generation using contact dynamics models; title: 5.5 Simulation results; title: 5.6 Conclusions; title: References; title: 6; title: Cooperative Behavior Schemes for Improving the Effectiveness of; title: Autonomous Wide Area Search Munitions; title: Daniel P. Gillen and David R. Jacques; title: 6.1 Introduction; title: 6.2 Baseline computer simulation; title: 6.3 Simulation modifications; title: 6.4 Applied response surface methodologies; title: 6.5 Results and analysis; title: 6.6 Conclusions and recommendations; title: References; title: 7; title: On a General Framework to Study Cooperative Systems; title: Victor Korotkich; title: 7.1 Introduction; title: 7.2 Structural complexity; title: 7.3 Parameter extension of the optimal algorithm; title: 7.4 Critical point in the parameter extension: the optimal algo-; title: rithm; title: 7.5 Structural complexity of cooperative systems versus optimiza-; title: tion problems; title: 7.6 Conclusion; title: References; title: 8; title: Cooperative Multi-agent Constellation Formation Under Sensing; title: and Communication Constraints; title: Lit-Hsin Loo, Erwei Lin, Moshe Kam & Pramod Varshney; title: 8.1 Introduction; title: 8.2 Group formation by autonomous homogeneous agents; title: 8.3 The noiseless full-information case; title: 8.4 Limitations on communications and sensing; title: 8.5 Limitation of communications; title: 8.6 Oscillations due to sensing limitation; title: 8.7 Group formation with partial view; title: 8.8 The use of 'meeting point' for target assignment; title: 8.9 Conclusion; title: References; title: 9; title: An Introduction to Collective and Cooperative Systems; title: Robert Murphey; title: 9.1 Preliminaries in game and team theory; title: 9.2 Collective systems.; title: 9.3 Precedence, hierarchy, and supervision; title: 9.4 Summary; title: References; title: 10; title: Cooperative Aircraft Control for Minimum Radar Exposure; title: Meir Pachter and Jeffrey Hebert; title: 10.1 Single vehicle radar exposure minimization; title: 10.2 Multiple vehicle isochronous rendezvous; title: 10.3 Conclusion; title: References; title: 11; title: Robust Recursive Bayesian Estimation and Quantum Minimax Strategies; title: P. Pardalos, V. Yatsenko and S. Butenko; title: 11.1 Introduction; title: 11.2 Differential geometry of Bayesian estimation; title: 11.3 Optimal recursive estimation; title: 11.4 Quantum realization of minimax Bayes strategies; title: 11.5 Concluding remarks; title: References; title: 12; title: Cooperative Control for Autonomous Air Vehicles; title: Kevin Passino Marios Polycarpou, David Jacques, Meir Pachter, Yang.; title: Yanli Yang, Matt Flint and Michael Baum; title: 12.1 Introduction; title: 12.2 Autonomous munition problem; title: 12.3 Cooperative control via distributed learning and planning; title: 12.4 Stable vehicular swarms; title: 12.5 Biomimicry of foraging for cooperative control; title: 12.6 Concluding remarks; title: References; title: 13; title: Optimal Risk Path Algorithms; title: Michael Zabarankin, Stanislav Uryasev, Panos Pardalos; title: 13.1 Model description and setup of the optimization problem; title: 13.2 Analytical solution approach for the risk path optimization; title: problem; title: 13.3 Discrete optimization approach for optimal risk path genera-; title: tion with a constraint on the length; title: 13.4 Concluding remarks.