Quantifying Network Controllability and Observability Using Optimal Control and Estimation Metrics

Abstract

The goal of this project is to develop methods to quantify network controllability and observability using optimal control and estimation metrics. Specifically, the three major goals of the project are:(1) quantify and visualize network controllability and observability using metrics based on fundamental optimal feedback control and state estimation problems; (2) develop algorithms for design of optimal sensor and actuator topologies and feedback information structures based on these metrics; (3) illustrate the theoretical and methodological results with comprehensive case studies in various application domains, including intelligence, surveillance, and reconnaissance scenarios involving autonomous mobile robot teams; neuronal brain networks; and electric power grids and microgrids.

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Document Details

Document Type
Technical Report
Publication Date
Oct 14, 2021
Accession Number
AD1200873

Entities

People

  • Tyler Summers

Organizations

  • University of Texas at Dallas

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Abstracts
  • Actuators
  • Algorithms
  • Artificial Intelligence
  • Autonomous Systems
  • Case Studies
  • Control Systems
  • Control Systems Engineering
  • Detectors
  • Kalman Filters
  • Load Monitoring
  • Mathematical Analysis
  • Model Predictive Control
  • Motion Planning
  • Multiagent Systems
  • Nonlinear Dynamics
  • Nonlinear Model Predictive Control
  • Robotics
  • Students
  • Trajectories
  • Unmanned Vehicles

Fields of Study

  • Computer science
  • Engineering

Readers

  • Control Systems Engineering.
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control