slide1
Download
Skip this Video
Download Presentation
Optimal Distributed State Estimation and Control, in the Presence of Communication Costs

Loading in 2 Seconds...

play fullscreen
1 / 20

Optimal Distributed State Estimation and Control, in the Presence of Communication Costs - PowerPoint PPT Presentation


  • 109 Views
  • Uploaded on

Optimal Distributed State Estimation and Control, in the Presence of Communication Costs. Nuno C. Martins. [email protected] Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland, College Park.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Optimal Distributed State Estimation and Control, in the Presence of Communication Costs' - rosalyn-rowland


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

Optimal Distributed State Estimation and Control,

in the Presence of Communication Costs

Nuno C. Martins

[email protected]

Department of Electrical and Computer Engineering

Institute for Systems Research

University of Maryland, College Park

AFOSR, MURI Kickoff Meeting, Washington D.C., September 29, 2009

slide2

Introduction

  • Setup is a network whose nodes
  • might comprise of:
  • Linear dynamic systems
  • Sensors with transmission
  • capabilities
  • Receivers including state
  • estimator

A Simple Configuration:

slide3

Introduction

  • Setup is a network whose nodes
  • might comprise of:
  • Linear dynamic systems
  • Sensors with transmission
  • capabilities
  • Receivers including state
  • estimator

A Simple Configuration:

  • Applications:
  • Tracking of stealthy aerial vehicles via (costly)
  • highly encrypted channels.
slide4

Introduction

  • Setup is a network whose nodes
  • might comprise of:
  • Linear dynamic systems
  • Sensors with transmission
  • capabilities
  • Receivers including state
  • estimator

A Simple Configuration:

  • Applications:
  • Tracking of stealthy aerial vehicles via (costly)
  • highly encrypted channels.
  • Distributed learning and control over power
  • limited networks.

NSF CPS: Medium 1.5M

Ant-Like Microrobots - Fast, Small, and Under Control

PI: Martins, Co PIs: Abshire, Smella, Bergbreiter

slide5

Introduction

  • Setup is a network whose nodes
  • might comprise of:
  • Linear dynamic systems
  • Sensors with transmission
  • capabilities
  • Receivers including state
  • estimator

A Simple Configuration:

  • Applications:
  • Tracking of stealthy aerial vehicles via (costly)
  • highly encrypted channels.
  • Distributed learning and control over power
  • limited networks.
  • Optimal information sharing in organizations.
slide6

A Simple Configuration:

  • Setup is a network whose nodes
  • might comprise of:
  • Linear dynamic systems
  • Sensors with transmission
  • capabilities
  • Receivers including state
  • estimator

Ultimately, we want to tackle general

instances of the multi-agent case.

slide7

A New Method for Certifying Optimality

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Optimal solution:

Transmit

Erasure

time

Transmit

slide8

A New Method for Certifying Optimality

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Optimal solution:

Transmit

Numerical method to compute

Optimal thresholds

Erasure

time

Transmit

slide9

A New Method for Certifying Optimality

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Optimal solution (a modified Kalman F.):

yes

Erasure?

Execute K.F.

no

slide10

A New Method for Certifying Optimality

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Past work:

slide11

A New Method for Certifying Optimality

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Past work:

Issai Schur

Key to our proof is the use

of majorization theory.

Frigyes Riesz

slide12

Recent Extensions

Tandem Topology

slide13

Recent Extensions

Tandem Topology

Threshold policy

Memoryless forward

Modified K.F.

Optimal

slide14

Recent Extensions

Tandem Topology

Threshold policy

Memoryless forward

Modified K.F.

Optimal

Control with communication costs (Lipsa, Martins, Allerton’09)

slide15

Problems with Non-Classical Information Structure

Multiple-stage Gaussian test channel

slide16

Problems with Non-Classical Information Structure

Multiple-stage Gaussian test channel

Lipsa and Martins, CDC’08

slide17

Summary and Future Work

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Extensions:

  • Future directions:
  • -More General Topologies, Including Loops
slide18

Summary and Future Work

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Extensions:

Future directions:

-More General Topologies, Including Loops

-Optimal Distributed Function Agreement with Communication Costs and Partial Information

slide19

Summary and Future Work

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Extensions:

  • Future directions:
  • -More General Topologies, Including Loops
  • -Optimal Distributed Function Agreement with Communication Costs and Partial Information
  • Game convergence and performance analysis
slide20

Summary and Future Work

Major results:

Nonlinear, non-convex.

Optimality was a long standing open problem.

Solution is provided in:

G. M. Lipsa, N. C. Martins, “Certifying the Optimality of a Distributed State Estimation

Scheme via Majorization Theory”, submitted to TAC, 2009

Thank you

Extensions:

  • Future directions:
  • -More General Topologies, Including Loops
  • -Optimal Distributed Function Agreement with Communication Costs and Partial Information
  • Include Adversarial Action (Game Theoretic Approach)
ad