Cooperative Control and Optimization

by Murphey, Robert; Pardalos, Panos M.

• ISBN13: 9781402005497
• ISBN10: 1402005490
• Format: Hardcover
• Copyright: 2002-05-01
• Publisher: Kluwer Academic Pub

Summary

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.

Table of Contents

Preface	xi
Cooperative Control for Target Classification	1	(20)
P.R. Chandler
M. Pachter
Kendall D. Nygard
Dharba Swaroop
Introduction	2	(2)
Joint classification	4	(5)
Assignment	9	(2)
Hierarchical architecture	11	(3)
Simulation	14	(1)
Classification issues	15	(1)
Conclusions	16	(5)
References	19	(2)
Guillotine Cut in Approximation Algorithms	21	(14)
Xiuzhen Cheng
Ding-Zhu Du
Joon-Mo Kim
Hung Quang Ngo
Introduction	21	(1)
Rectangular partition and guillotine cut	22	(4)
1-Guillotine cut	26	(4)
m-Guillotine cut	30	(1)
Portals	31	(4)
References	33	(2)
Unmanned Aerial Vehicles: Autonomous Control Challenges, Researcher's Perspective	35	(20)
Bruce T. Clough
Introduction	36	(1)
Background	36	(3)
The Challenges	39	(3)
How are we approaching these challenges?	42	(6)
Where are we heading from here?	48	(7)
References	51	(2)
Appendix: Notes	53	(2)
Optimal periodic stochastic filtering with GRASP	55	(18)
Paola Festa
Giancarlo Raiconi
Introduction and problem statement	56	(2)
Two interesting particular cases	58	(7)
Discrete problem formulation	65	(3)
Numerical results	68	(5)
References	71	(2)
Cooperative Control of Robot Formations	73	(22)
Rafael Fierro
Peng Song
Aveek Das
Vijay Kumar
Introduction	73	(2)
Framework for cooperative control	75	(1)
Formation control	76	(8)
Trajectory generation using contact dynamics models	84	(3)
Simulation results	87	(1)
Conclusions	88	(7)
References	91	(4)
Cooperative Behavior Schemes for Improving the Effectiveness of Autonomous Wide Are Search Munitions	95	(26)
Daniel P. Gillen
David R. Jacques
Introduction	96	(3)
Baseline computer simulation	99	(1)
Simulation modifications	100	(7)
Applied response surface methodologies	107	(5)
Results and analysis	112	(6)
Conclusions and recommendations	118	(3)
References	119	(2)
General Framework to Study Cooperative Systems	121	(22)
Victor Korotkich
Introduction	121	(3)
Structural complexity	124	(3)
Parameter extension of the optimal algorithm	127	(2)
Critical point in the parameter extension: the optimal algorithm	129	(2)
Structural complexity of cooperative systems versus optimization problems	131	(9)
Conclusion	140	(3)
References	141	(2)
Cooperative Multi-agent Constellation Formation Under Sensing and Communication Constraints	143	(28)
Lit-Hsin Loo
Erwei Lin
Moshe Kam
Pramod Varshney
Introduction	144	(1)
Group formation by autonomous homogeneous agents	145	(2)
The noiseless full-information case	147	(2)
Limitations on communications and sensing	149	(4)
Limitation of communications	153	(3)
Oscillations due to sensing limitation	156	(1)
Group formation with partial view	156	(5)
The use of 'meeting point' for target assignment	161	(2)
Conclusion	163	(8)
References	167	(4)
An Introduction to Collective and Cooperative Systems	171	(28)
Robert Murphey
Preliminaries in game and team theory	172	(8)
Collective systems	180	(5)
Precedence, hierarchy, and supervision	185	(8)
Summary	193	(6)
References	195	(4)
Cooperative Aircraft Control for Minimum Radar Exposure	199	(14)
Meir Pachter
Jeffrey Hebert
Single vehicle radar exposure minimization	200	(7)
Multiple vehicle isochronous rendezvous	207	(1)
Conclusion	208	(5)
References	211	(2)
Robust Recursive Bayesian Estimation and Quantum Minimax Strategies	213	(20)
P. Pardalos
V. Yatsenko
S. Butenko
Introduction	214	(1)
Differential geometry of Bayesian estimation	215	(2)
Optimal recursive estimation	217	(8)
Quantum realization of minimax Bayes strategies	225	(4)
Concluding remarks	229	(4)
References	231	(2)
Cooperative Control for Autonomous Air Vehicles	233	(40)
Kevin Passino
Marios Polycarpou
David Jacques
Meir Pachter
Yang Liu
Yanli Yang
Matt Flint
Michael Baum
Introduction	234	(5)
Autonomous munition problem	239	(3)
Cooperative control via distributed learning and planning	242	(16)
Stable vehicular swarms	258	(5)
Biomimicry of foraging for cooperative control	263	(4)
Concluding remarks	267	(6)
References	269	(4)
Optimal Risk Path Algorithms	273	(26)
Michael Zabarankin
Stanislav Uryasev
Panos Pardalos
Model description and setup of the optimization problem	276	(3)
Analytical solution approach for the risk path optimization problem	279	(5)
Discrete optimization approach for optimal risk path generation with constraint on the length	284	(9)
Concluding remarks	293	(6)
References	297	(2)
Appendix	299