Controlling Collective Dynamics of Underactuated Ensembles

Abstract

This project focused on the development of a systematic framework and a set of unifying principles and approaches to advance our understanding of complex ensemble systems and ability to control their collective behavior. The motivation was driven by the applications in rapidly expanding and emerging transdisciplinary domains, such as quantum control, neurostimulation, chronobiology, and robotics, wherein engineers, experimentalists, and clinical practitioners exert exogenous inputs (e.g. electromagnetic pulses, electrical microsimulation, and light protocols) to excite, perturb and, furthermore, optimally control the activity or the spatiotemporal structure of an ensemble system consisting of a large number of dynamical units towards scientific or therapeutic endpoints. The major challenge concerning this class of problems was that one cannot send control signals to individual systems in the ensemble, but only to the ensemble as a whole. This project (1) established new methods for examining fundamental properties, such as ensemble controllability, to understand the theoretical limits of the extent to which ensemble activity and dynamic structures can be perturbed with an exogenous input; and (2) developed effective computational methods for solving optimal control problems involving ensemble systems and networks. The research substantially advanced our understanding of complex ensemble systems and directly contributed to new developments in control and systems theory, which supports the research mission of the AFOSR.

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Document Details

Document Type
Technical Report
Publication Date
Jul 28, 2020
Accession Number
AD1107053

Entities

People

  • Jr-Shin Li

Organizations

  • Washington University in St. Louis

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Air Force Research Laboratories
  • Buildings And Structures
  • Computational Science
  • Department Of Defense
  • Dynamics
  • Electromagnetic Pulses
  • Military Research
  • Scientific Research
  • Standards
  • Universities

Readers

  • Neural Network Machine Learning.
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • Autonomy
  • Autonomy - Autonomous System Control
  • Quantum Computing
  • Quantum Science - Quantum Dots