Biologically-Inspired Optimal Control via Intermittent Cooperation

Abstract

We investigate the solution of a large class of fixed-final-state optimal control problems by a group of cooperating dynamical systems. We present a pursuit-based algorithm inspired by the foraging behavior of ants that requires each system-member of the group to solve a finite number of optimization problems as it follows other members of the group from a starting to a final state. Our algorithm, term "sampled local pursuit", is iterative and leads the group to a locally optimal solution, starting from an initial feasible trajectory. The proposed algorithm is broad in its applicability and generalizes previous results; it requires only short-range sensing and limited interactions between group members, and avoids the need for a "global map" of the environment or manifold on which the group evolves. We include simulations that illustrate the performance of our algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA438963

Entities

People

  • Cheng Shao
  • D. Hristu-varsakelis

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Autonomous Systems
  • Computations
  • Control Systems
  • Convergence
  • Cooperation
  • Coordinate Systems
  • Dynamics
  • Environment
  • Intervals
  • Mechanical Engineering
  • Military Research
  • Psychology
  • Time Intervals
  • Trajectories
  • Universities

Readers

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