Recent developments in cooperative control and optimization
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Recent developments in cooperative control and optimization
（Cooperative systems, v. 3）
Kluwer Academic Publishers, c2004
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Includes bibliographical references
Description and Table of Contents
Description
Over the past several years, cooperative control and optimization has un questionably been established as one of the most important areas of research in the military sciences. Even so, cooperative control and optimization tran scends 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. And no wonder, cooperative systems are among the hardest systems control science has endeavored to study, hence creative approaches to model ing, analysis, and synthesis are a must! The definition of cooperation itself is a slippery issue. As you will see in this and previous volumes, cooperation has been cast into many different roles and therefore has assumed many diverse meanings. Perhaps the most we can say which unites these disparate concepts is that cooperation (1) requires more than one entity, (2) the entities must have some dynamic behavior that influences the decision space, (3) the entities share at least one common objective, and (4) entities are able to share information about themselves and their environment. Optimization and control have long been active fields of research in engi neering.
Table of Contents
1 A Hybrid Projected Gradient-Evolutionary Search Algorithm for Capacitated Multi-Source Multi-UAVs Scheduling with Time Windows.- 1.1 Introduction.- 1.2 Mathematical Programming Formulation For Capacitated Multi-UAV Routing With Time Windows.- 1.3 Hybrid Projected Gradient-Evolutionary Search Algorithm.- 1.4 Simulation Results.- 1.5 Conclusion.- 2 Progress in Cooperative Volume Holographic Imaging.- 2.1 Introduction.- 2.2 Formation of Volume Holographic Images.- 2.3 Volume Holographic Imaging with Planar Reference holograms.- 2.4 Cooperative Processing of Volume Holographic Images Using the Pseudo-Inverse Method.- 2.5 Conclusions and Future Work.- 3 Properties of No-Depot Min-?ax 2-Traveling-Salesmen Problem.- 3.1 Introduction.- 3.2 Characteristic Function for No-Depot Min-Max 2-TSP.- 3.3 Threshold Characteristic Function.- 3.4 Constant Graphs.- 3.5 Interpretation of Threshold Self-Dual Monotonic Boolean Functions.- 3.6 Conclusion.- 4 A New Heuristic for the Minimum Connected Dominating Set Problem on Ad Hoc Wireless Networks.- 4.1 Introduction.- 4.2 Algorithm for the MCDS Problem.- 4.3 A Distributed Implementation.- 4.4 Numerical Experiments.- 4.5 Concluding Remarks.- 5 A Platform for Cooperative and Coordinated Control of Multiple Vehicles: The Caltech Multi-Vehicle Wireless Testbed.- 5.1 Introduction.- 5.2 Vehicle Hardware.- 5.3 Lab Positioning System.- 5.4 Onboard Sensing.- 5.5 Electronics.- 5.6 Software Environment.- 5.7 Communications.- 5.8 Modeling and Control.- 5.9 Future Directions.- 5.10 Conclusion.- 6 Churning: Repeated Optimization and Cooperative Instability.- 6.1 Introduction.- 6.2 Problem Formulation.- 6.3 Receding Horizon Instability and Churning.- 6.4 Churning Instability.- 6.5 Limiting Churn.- 6.6 Example.- 6.7 Discussion and Conclusion.- 7 A Hospitability Map Approach for Estimating a Mobile Targets Location.- 7.1 Introduction.- 7.2 Approach.- 7.3 Simulation Results.- 7.4 Future Research.- 8 Information Theoretic Organization Principles for Autonomous Multiple-Agents.- 8.1 Introduction.- 8.2 Background on Information Theory.- 8.3 Nonparametric Estimation of Renyi's Entropy.- 8.4 Information Particles.- 8.5 Self-Organization of Multiple Agents Using Particle Interaction Principles.- 8.6 Case Study Using a Particular Implementation.- 8.7 Conclusions.- 9 Distributed Agreement Strategies for Cooperative Control: Modeling and Scalability Analysis.- 9.1 Multi-UAV Cooperative Control Problem Model.- 9.2 Cooperative Agreement Strategies.- 9.3 Concluding Remarks.- 10 An Integer Programming Model for Assigning Unmanned Air Vehides to Tasks.- 10.1 Model.- 10.2 Model Characteristics.- 10.3 Solution Method.- 10.4 Computational Experiments.- 11 A Theoretical Foundation for Cooperative Search, Classification, and Target Attack.- 11.1 Introduction.- 11.2 Modelling.- 11.3 Scenario 1.- 11.4 Scenario 2.- 11.5 Scenario 3.- 11.6 Scenario 4.- 11.7 Scenario 5.- 11.8 Scenario 6.- 11.9 Cooperative Control.- 11.10 Conclusion.- 12 Cooperative Real-Time Task Allocation Among Groups of UAVs.- 12.1 Introduction.- 12.2 Algorithm Description.- 12.3 Performance Measures.- 12.4 Simulation Results.- 12.5 Decentralization Approach.- 12.6 Conclusion and Future Work.- Appendix: Derivation of TOP Update Equations.- 13 Use of Conditional Value-at-Risk in Stochastic Programs with Poorly Defined Distributions.- 13.1 Deterministic Weapon-Target Assignment Problem.- 13.2 Two-Stage Stochastic WTA Problem.- 13.3 Two-Stage WTA Problem with Uncertainties in Specified Distributions.- 13.4 Case Study.- 14 Sensitivity Analysis of Partially Deployed Slowdown Warning Mechanisms for Vehicle Platoons.- 14.1 Introduction.- 14.2 Notation and Problem Formulation.- 14.3 DP and NDP Formulations.- 14.4 Main Results.- 14.5 Complexity Reduction: Multilevel Path Planning.- 14.6 Discussion.- 14.7 Conclusion and Future Directions.- Appendix: Proof of Lemma 4.1.- 15 Multi-Target Assignment and Path Planning for Groups of UAVs.- 15.1 Introduction.- 15.2 Simulation Results.- 15.3 Conclusion and Future Work.- 16 Objective Functions for Bayesian Control-Theoretic Sensor Management, II: MHC-Like Approximation.- 16.1 Introduction.- 16.2 Single-Sensor, Single-Target Bayesian Control.- 16.3 Multisensor-Multitarget Bayesian Control.- 16.4 Single-Step Objective Functions.- 16.5 Multistep Objective Functions.- 16.6 Sensor Management With MHC-Like Filters.- 16.7 Mathematical Proofs.- 16.8 Conclusions.- 17 Tracking Environmental Level Sets with Autonomous Vehicles.- 17.1 Introduction.- 17.2 Energy Minimizing Curves in Image Processing.- 17.3 Agent Based Motion via "Virtual" Contours.- 17.4 Implementation and Communication.- 17.5 Cooperative Motion Simulations.- 17.6 Boundary Tracking without Communication.- 17.7 Robustness under Sensor Noise.- 17.8 Conclusions and Future Work.- 18 Cyclic Linearization and Decomposition of Team Game Models.- 18.1 Introduction.- 18.2 General Properties.- 18.3 The Cyclic Linearization Algorithm.- 18.4 Inaccurate Linearized Realizations of the Cyclic Decomposition.- 19 Optimal Path Planning in a Threat Environment.- 19.1 Introduction.- 19.2 Model Development.- 19.3 Calculus of Variations Approach.- 19.4 Network Flow Optimization Approach.- 19.5 Numerical Experiments.- 19.6 Analysis of Computational Results.- 19.7 Conclusions.- Appendix: Minimization of a Functional with Nonholonomic Constraint and Movable End Point.- 20 Nonlinear Dynamics of Sea Clutters and Detection of Small Targets.- 20.1 Introduction.- 20.2 Method.- 20.3 Simulation Results.- 20.4 Experimental Results.- 20.5 Mathematical and Physical Models of Sea Clutter.- 20.6 Discussion and Conclusion.- 21 Tree-Based Algorithms for the Multidimensional Assignment Problem.- 21.1 Introduction.- 21.2 Tree Representations.- 21.3 Branch and Bound Algorithms.- 21.4 Greedy Randomized Adaptive Search Procedure.- 21.5 Concluding Remarks.- 22 Predicting Pop Up Threats From An Adaptive Markov Model.- 22.1 Introduction.- 22.2 Modeling of Pop Up Targets.- 22.3 Adaptive Markov Model.- 22.4 Modeling of UAVs.- 22.5 Generating Data for Red Pop Up Locations.- 22.6 Experimental Results.- 22.7 Conclusion.
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