Hybrid Control for Multi-Agent Systems in Complex Sensing Environments

Abstract

The overall program provided systematic and computationally responsive research aimed at control of multi-agent systems, and more generally systems that have both discrete and continuous dynamics, using convex optimization and semidefinite programming as the foundation. The program has produced research results in several domains. In the area of distributed control several results were obtained: new stabilization methods were developed for systems in which dynamical agents interact over bandlimited channels; synthesis methods were also developed for metric-based performance optimization of distributed systems over graphs. Systems with simultaneously network latency and finite precision sensing were targeted and algorithms were developed that can guarantee stabilization in these limited information scenarios. For switched systems a separate program accomplishment was the development of equivalence and separation principles for joint synthesis of switching rules and policies. Also, the first known exact convex conditions for receding horizon control of switched systems were found, together with design algorithms for optimizing metric-based performance. New decidability and verification results for general hybrid setting were developed.

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Document Details

Document Type
Technical Report
Publication Date
Feb 28, 2012
Accession Number
ADA567715

Entities

People

  • Geir E. Dullerud

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • C4I
  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Automata
  • Closed Loop Systems
  • Coding
  • Communication Channels
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Control Systems
  • Data Rate
  • Differential Equations
  • Equations
  • Hybrid Systems
  • Linear Systems
  • Mechanical Engineering
  • Multiagent Systems

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation