Real-Time Optimization in Complex Stochastic Environment

Abstract

The research reported here aims at enabling a systematic on-line use of optimization techniques for real-time applications in complex stochastic environments that recognizes requirements for new generations of systems critical to the national infrastructure and consistent with the emerging information-based, network-centric view of warfare. The main outcomes of the project are: (a) Asynchronous, event-driven distributed optimization algorithms with the ability to escape frequently occurring local equilibria. (b) Novel on-line trajectory optimization schemes for cooperative multi-agent systems which are scalable and robust with respect to the uncertainty model used. (c) A general-purpose event-driven receding-horizon optimization framework. (d) A general unified Infinitesimal Perturbation Analysis framework for efficient gradient-based optimization of complex stochastic systems.

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Document Details

Document Type
Technical Report
Publication Date
Jun 24, 2015
Accession Number
ADA623572

Entities

People

  • Christos G. Cassandras

Organizations

  • Boston University

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computational Science
  • Control Systems
  • Control Systems Engineering
  • Detectors
  • Electric Vehicles
  • Electronic Mail
  • Engineering
  • Environment
  • Evolutionary Algorithms
  • Multiagent Systems
  • Optimization
  • Sensor Networks
  • Systems Engineering
  • Unmanned Aerial Vehicles
  • Wireless Sensor Networks

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Joint Military Operations and Doctrine.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.