Recent Developments in Cooperative Control and Optimization,9781402076442

Recent Developments in Cooperative Control and Optimization

by ; ;
ISBN13: 9781402076442
ISBN10: 1402076444
Format: Hardcover
Pub. Date: 2003-11-01
Publisher(s): Kluwer Academic Pub
List Price: $169.99

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Summary

Over the past several years, cooperative control and optimization has unquestionably been established as one of the most important areas of research in the military sciences. Even so, cooperative control and optimization transcends the military in its scope - having become quite relevant to a broad class of systems with many exciting commercial applications. One reason for all the excitement is that research has been so incredibly diverse - spanning many scientific and engineering disciplines. This latest volume in the "Cooperative Systems" book series clearly illustrates this trend towards diversity and creative thought. It's no wonder, cooperative systems are among the hardest systems control science has endeavored to study, hence creative approaches to modeling, analysis, and synthesis are a must. Audience: This volume is suitable for faculty, graduate students, and researchers in optimization and control, computer science and engineering.

Table of Contents

Preface xiii
A Hybrid Projected Gradient-Evolutionary Search Algorithm for Capacitated Multi-Source Multi-UAVs Scheduling with Time Windows
1(22)
Ali Ahmadzadeh
Bijan Sayyar-Roudsari
Abdollah Homaifar
Introduction
2(2)
Mathematical Programming Formulation For Capacitated Multi-UAV Routing With Time Windows
4(3)
Hybrid Projected Gradient-Evolutionary Search Algorithm
7(9)
Simulation Results
16(2)
Conclusion
18(5)
Progress in Cooperative Volume Holographic Imaging
23(22)
George Barbastathis
Arnab Sinha
Introduction
23(3)
Formation of Volume Holographic Images
26(5)
Volume Holographic Imaging with Planar Reference holograms
31(7)
Cooperative Processing of Volume Holographic Images Using the Pseudo--Inverse Method
38(5)
Conclusions and Future Work
43(2)
Properties of No-Depot Min-Max 2-Traveling-Salesmen Problem
45(16)
Vladimir Bugera
Introduction
45(2)
Characteristic Function for No-Depot Min-Max 2-TSP
47(3)
Threshold Characteristic Function
50(3)
Constant Graphs
53(1)
Interpretation of Threshold Self-Dual Monotonic Boolean Functions
54(4)
Conclusion
58(3)
A New Heuristic for the Minimum Connected Dominating Set Problem on Ad Hoc Wireless Networks
61(14)
Sergiy Butenko
Xiuzhen Cheng
Carlos A.S Oliveira
P. M. Pardalos
Introduction
62(2)
Algorithm for the MCDS Problem
64(2)
A Distributed Implementation
66(2)
Numerical Experiments
68(3)
Concluding Remarks
71(4)
A Platform for Cooperative and Coordinated Control of Multiple Vehicles: The Caltech Multi-Vehicle Wireless Testbed
75(30)
Timothy Chung
Lars Cremean
William B. Dunbar
Zhipu Jin
Eric Klavins
David Moore
Abhishek Tiwari
Dave van Gogh
Stephen Waydo
Introduction
75(5)
Vehicle Hardware
80(6)
Lab Positioning System
86(1)
Onboard Sensing
87(2)
Electronics
89(3)
Software Environment
92(2)
Communications
94(1)
Modeling and Control
95(6)
Future Directions
101(1)
Conclusion
102(3)
Churning: Repeated Optimization and Cooperative Instability
105(12)
J. Willard Curtis
Introduction
105(1)
Problem Formulation
106(2)
Receding Horizon Instability and Churning
108(1)
Churning Instability
109(2)
Limiting Churn
111(1)
Example
112(2)
Discussion and Conclusion
114(3)
A Hospitability Map Approach for Estimating a Mobile Targets Location
117(8)
Martin Eilders
Jeff Layne
Zaher M. Kassas
Umit Ozguner
Introduction
118(1)
Approach
119(1)
Simulation Results
120(3)
Future Research
123(2)
Information Theoretic Organization Principles for Autonomous Multiple-Agents
125(20)
Deniz Erdogmus
Jose C. Principe
Rajvignesh Thogulua
Introduction
125(2)
Background on Information Theory
127(1)
Nonparametric Estimation of Renyi's Entropy
128(1)
Information Particles
129(3)
Self-Organization of Multiple Agents Using Particle Interaction Principles
132(3)
Case Study Using a Particular Implementation
135(6)
Conclusions
141(4)
Distributed Agreement Strategies for Cooperative Control: Modeling and Scalability Analysis
145(22)
Sriram Ganapathy
Kevin M. Passino
Multi-UAV Cooperative Control Problem Model
146(12)
Cooperative Agreement Strategies
158(5)
Concluding Remarks
163(4)
An Integer Programming Model for Assigning Unmanned Air Vehicles to Tasks
167(8)
Michael J. Hennebry
Ahmed Kamel
Kendall E. Nygard
Model
168(1)
Model Characteristics
169(1)
Solution Method
170(1)
Computational Experiments
170(5)
A Theoretical Foundation for Cooperative Search, Classification, and Target Attack
175(32)
David R. Jacques
Meir Pachter
Introduction
176(2)
Modelling
178(3)
Scenario 1
181(7)
Scenario 2
188(3)
Scenario 3
191(4)
Scenario 4
195(2)
Scenario 5
197(4)
Scenario 6
201(2)
Cooperative Control
203(1)
Conclusion
204(3)
Cooperative Real-Time Task Allocation Among Groups of UAVs
207(18)
Yan Jin
Marios M. Polycarpou
Ali A. Minai
Introduction
208(7)
Algorithm Description
215(2)
Performance Measures
217(1)
Simulation Results
218(1)
Decentralization Approach
218(3)
Conclusion and Future Work
221(4)
Appendix: Derivation of Top Update Equations
222(3)
Use of Conditional Value-at-Risk in Stochastic Programs with Poorly Defined Distributions
225(18)
Pavlo Krokhmal
Robert Murphey
Panos Pardalos
Stanislav Uryasev
Deterministic Weapon-Target Assignment Problem
227(4)
Two-Stage Stochastic WTA Problem
231(1)
Two-Stage WTA Problem with Uncertainties in Specified Distributions
232(4)
Case Study
236(7)
Sensitivity Analysis of Partially Deployed Slowdown Warning Mechanisms for Vehicle Platoons
243(18)
Vishwesh Kulkarni
Jan De Mot
Nicola Elia
Eric Feron
James Paduano
Introduction
244(1)
Notation and Problem Formulation
245(2)
DP and NDP Formulations
247(2)
Main Results
249(3)
Complexity Reduction: Multilevel Path Planning
252(1)
Discussion
253(1)
Conclusion and Future Directions
254(7)
Appendix: Proof of Lemma 4.1
255(6)
Multi-Target Assignment and Path Planning for Groups of UAVs
261(12)
Theju Maddula
Ali A. Minai
Marios M. Polycarpou
Introduction
262(7)
Simulation Results
269(1)
Conclusion and Future Work
269(4)
Objective Functions for Bayesian Control-Theoretic Sensor Management, II: MHC-Like Approximation
273(44)
Ronald Mahler
Introduction
273(5)
Single-Sensor, Single-Target Bayesian Control
278(8)
Multisensor-Multitarget Bayesian Control
286(8)
Single-Step Objective Functions
294(6)
Multistep Objective Functions
300(4)
Sensor Management With MHC-Like Filters
304(6)
Mathematical Proofs
310(4)
Conclusions
314(3)
Tracking Environmental Level Sets with Autonomous Vehicles
317(16)
Daniel Marthaler
Andrea L. Bertozzi
Introduction
317(2)
Energy Minimizing Curves in Image Processing
319(1)
Agent Based Motion via ``Virtual'' Contours
320(2)
Implementation and Communication
322(3)
Cooperative Motion Simulations
325(1)
Boundary Tracking without Communication
326(2)
Robustness under Sensor Noise
328(2)
Conclusions and Future Work
330(3)
Cyclic Linearization and Decomposition of Team Game Models
333(16)
Athanasios Migdalas
Introduction
333(3)
General Properties
336(2)
The Cyclic Linearization Algorithm
338(3)
Inaccurate Linearized Realizations of the Cyclic Decomposition
341(8)
Optimal Path Planning in a Threat Environment
349(58)
Rob Murphey
Stanislav Uryasev
Michael Zabarankin
Introduction
350(4)
Model Development
354(3)
Calculus of Variations Approach
357(18)
Network Flow Optimization Approach
375(11)
Numerical Experiments
386(11)
Analysis of Computational Results
397(3)
Conclusions
400(7)
Appendix: Minimization of a Functional with Nonholonomic Constraint and Movable End Point
402(5)
Nonlinear Dynamics of Sea Clutters and Detection of Small Targets
407(20)
Panos M. Pardalos
Vitaliy A. Yatsenko
Don A. Grundel
Introduction
408(1)
Method
409(9)
Simulation Results
418(3)
Experimental Results
421(2)
Mathematical and Physical Models of Sea Clutter
423(1)
Discussion and Conclusion
424(3)
Tree-Based Algorithms for the Multidimensional Assignment Problem
427(22)
Eduardo L. Pasiliao
Introduction
427(4)
Tree Representations
431(3)
Branch and Bound Algorithms
434(5)
Greedy Randomized Adaptive Search Procedure
439(6)
Concluding Remarks
445(4)
Predicting Pop Up Threats From An Adaptive Markov Model
449(1)
Shankar K. Subramanian
Jose B. Cruz
Phillip R. Chandler
Meir Pachter
Introduction
450(1)
Modeling of Pop Up Targets
450(1)
Adaptive Markov Model
451(2)
Modeling of UAVs
453(2)
Generating Data for Red Pop Up Locations
455(1)
Experimental Results
456(2)
Conclusion
458

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