Cooperative Control of Distributed Multi-Agent Systems

by Editor: Jeff Shamma (Georgia Institute of Technology)

• ISBN13: 9780470724194
• ISBN10: 0470724196
• Format: eBook
• Copyright: 2008-04-01
• Publisher: Wiley-Interscience

Summary

The paradigm of 'multi-agent' cooperative control is the challenge frontier for new control system application domains, and as a research area it has experienced a considerable increase in activity in recent years. This volume, the result of a UCLA collaborative project with Caltech, Cornell and MIT, presents cutting edge results in terms of the "dimensions" of cooperative control from leading researchers worldwide. This dimensional decomposition allows the reader to assess the multi-faceted landscape of cooperative control.Cooperative Control of Distributed Multi-Agent Systems is organized into four main themes, or dimensions, of cooperative control: distributed control and computation, adversarial interactions, uncertain evolution and complexity management. The military application of autonomous vehicles systems or multiple unmanned vehicles is primarily targeted; however much of the material is relevant to a broader range of multi-agent systems including cooperative robotics, distributed computing, sensor networks and data network congestion control.Cooperative Control of Distributed Multi-Agent Systems offers the reader an organized presentation of a variety of recent research advances, supporting software and experimental data on the resolution of the cooperative control problem. It will appeal to senior academics, researchers and graduate students as well as engineers working in the areas of cooperative systems, control and optimization.

Table of Contents

List of Contributors

Preface

Introduction

Dimensions of cooperative control

Why cooperative control?

Dimensions of cooperative control

Future directions

Acknowledgements

References

Distributed Control and Computation

Design of behavior of swarms: From flocking to data fusion using microfilter networks

Introduction

Consensus problems

Flocking behavior for distributed coverage

Microfilter networks for cooperative data fusion

Acknowledgements

References

Connectivity and convergence of formations

Introduction

Problem formulation

Algebraic graph theory

Stability of vehicle formations in the case of time-invariant communication

Stability of vehicle formations in the case of time-variant communication

Stabilizing feedback for the time-variant communication case

Graph connectivity and stability of vehicle formations

Conclusion

Acknowledgements

References

Distributed receding horizon control: stability via move suppression

Introduction

System description and objective

Distributed receding horizon control

Feasibility and stability analysis

Conclusion

Acknowledgements

References

Distributed predictive control: synthesis, stability and feasibility

Introduction

Problem formulation

Distributed MPC scheme

DMPC stability analysis

Distributed design for identical unconstrained LTI subsystems

Ensuring feasibility

Conclusion

References

Task assignment for mobile agents

Acknowledgements

References

On the value of information in dynamic multiple-vehicle routing problems

Introduction

Problem formulation

Control policy description

Performance analysis in light load

A performance analysis for sTP, mTP/FG and mTP policies

Some numerical results

Conclusions

References

Optimal agent cooperation with local information

Introduction

Notation and problem formulation

Mathematical problem formulation

Algorithm overview and LP decomposition

Fixed point computation

Discussion and examples

Conclusion

Acknowledgements

References

Multiagent cooperation through egocentric modeling

Introduction

Centralized and decentralized optimization

Evolutionary cooperation

Analysis of convergence

Conclusion

Acknowledgements

References

Adversarial Interactions

Multi-vehicle cooperative control using mixed integer linear programming

Introduction

Vehicle dynamics

Obstacle avoidance

RoboFlag problems

Average case complexity

Discussion

Appendix: Converting logic into inequalities

Acknowledgements

References

LP-based multi-vehicle path planning with adversaries

Introduction

Problem formulation

Optimization set-up

LP-based path planning

Implementation

Conclusion

Acknowledgements

References

Characterization of LQG differential games with different information patterns

Introduction

Formulation of the discrete-time LQG game

Solution of the LQG game as the limit to the LEG Game

LQG game as the limit of the LEG Game

Correlation properties of the LQG game filter in the limit

Cost function propertiesùeffect of a perturbation in up

Performance of the Kalman filtering algorithm

Comparison with the Willman algorithm

Equilibrium properties of the cost function: the saddle interval

Conclusion

Acknowledgements

References

Uncertain Evolution

Modal estimation of jump linear systems: an information theoretic viewpoint

Estimation of a class of hidden markov models

Problem statement

Encoding and decoding

Performance analysis

Auxiliary results leading to the proof of theorem

Acknowledgements

References

Conditionally-linear filtering for mode estimation in jump-linear systems

Introduction

Conditionally-Linear Filtering

Mode-estimation for jump-linear systems

Numerical Example

Conclusion

Appendix A: Inner product of equation (14.14

Appendix B: Development of the filter equations (14.36) to (14.37

Acknowledgements

References

Cohesion of languages in grammar networks

Introduction

Evolutionary dynamics of languages

Topologies of language populations

Language structure

Networks induced by structural similarity

Conclusion

Acknowledgements

References

Complexity Management

Complexity management in the state estimation of multi-agent systems

Introduction

Motivating example

Basic concepts

Problem formulation

Problem solution

Example: the RoboFlag Drill

Existence of discrete state estimators on a lattice

Extensions to the estimation of discrete and continuous variables

Conclusion

Acknowledgements

References

Abstraction-based command and control with patch models

Introduction

Overview of patch models

Realization and verification

Human and artificial decision-making

Hierarchical control

Conclusion

References

Index

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