Cooperative Control of Distributed Multi-Agent Systems

by Shamma, Jeff

• ISBN13: 9780470060315
• ISBN10: 047006031X
• Edition: 1st
• Format: Hardcover
• Copyright: 2008-01-22
• Publisher: Wiley-Interscience

Summary

This book presents the results of a  "MURI" (Multidisciplinary University Research Initiative) projects on the topic.  The material is organized under four broad themes, or dimensions of cooperative control. These are: _ Distributed control & computation: The defining characteristic of cooperative control, namely, the distributed nature of decision making among interacting components. Adversarial interactions: A driving motivation for this project is autonomous vehicle systems operating in a possibly hostile environment. The presence of other hostile vehicles that are capable of strategic planning brings into play notions from non-cooperative game theory.   Uncertain evolution: The operating environment introduces significant uncertainty in the operation of autonomous vehicle systems and Complexity management: Cooperative control formulations, if taken at face value, often result in computationally prohibitive solutions.

Author Biography

Jeff Shamma's research interest is feedback control and systems theory. He received a Ph.D. in Systems Science and Engineering in 1988 from the Massachusetts Institute of Technology, Department of Mechanical Engineering. His previous faculty positions have included the University of Minnesota, Minneapolis, and the University of Texas, Austin. Since 1999, he has been with UCLA, where he is currently a Professor of Mechanical and Aerospace Engineering. He served as the MAE Department Vice Chair for Graduate Affairs from 2000-2002.  Jeff Shamma is also the recipient of the NSF Young Investigator Award (1992), a recipient of the American Automatic Control Council Donald P. Eckman Award (1996), a past Plenary Speaker at the American Control Conference (1998), and a Fellow of the IEEE (2006).  He has served on the editorial boards of the IEEE Transactions on Automatic Control and Systems & Control Letters.

Table of Contents

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

Introduction

Background

Problem statement

Assignment algorithm and results

Simulations

Conclusions

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

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