Distributed Learning, Extremum Seeking, and Model-Free Optimization for the Resilient Coordination of Multi-Agent Adversarial Groups

Abstract

This proposal focuses on the analysis and design of coordination algorithms for multiple agents deployed in adversarial environments. The multi-agent systems can represent miscellaneous autonomous and semi-autonomous vehicles that are remotely controlled by operators. These groups can be subject to attacks from other external agents leading to complex networked adversarial settings. The proposal presents work in two main areas: 1) the use of a class of receding-horizon type of algorithms to overcome the effect of a type of uncoordinated attackers on a multi-vehicle-operator group, and 2) the use of extremum seeking (ES) techniques to learn Nash equilibria in finitely- and infinitely-many player noncooperative games and to solve high-dimensional optimization problems. Extensions and applications of these techniques were developed during the realization of the project.

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Document Details

Document Type
Technical Report
Publication Date
Sep 07, 2016
Accession Number
AD1016962

Entities

People

  • Miroslav Krstić
  • Sonia Martı́nez

Organizations

  • University of California, San Diego

Tags

Communities of Interest

  • C4I
  • Cyber
  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Space

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Applied Mathematics
  • Autonomous Vehicles
  • Control Systems
  • Control Systems Engineering
  • Denial Of Service Attack
  • Distance Learning
  • Electronic Mail
  • Engineering
  • Environment
  • Learning
  • Light Sources
  • Linear Systems
  • Multiagent Systems
  • Optimization
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Game Theory.
  • Systems Analysis and Design

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control