A Local Pursuit Strategy for Bio-Inspired Optimal Control with Partially-Constrained Final State

Abstract

Inspired by the process by which ants gradually optimize their foraging trails, this report investigates the cooperative solution of a class of free-final time, partially-constrained final state optimal control problems by a group of dynamic systems. A class of cooperative, pursuit-based algorithms are proposed for finding optimal solutions by iteratively optimizing an initial feasible control. The proposed algorithms require only short-range, limited interactions between group members, avoid the need for a "global map" of the environment on which the group evolves, and solve an optimal control problem in "small" pieces, in a manner which will be made precise. The performance of the algorithms is illustrated in a series of simulations and laboratory experiments.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA439519

Entities

People

  • Cheng Shao
  • Dimitrios Hristu-varsakelis

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Algebraic Functions
  • Algorithms
  • Autonomous Systems
  • Computations
  • Control Systems
  • Convergence
  • Coordinate Systems
  • Engineering
  • Environment
  • Intervals
  • Mechanical Engineering
  • Optimization
  • Sequences
  • Simulations
  • Trajectories
  • Universities

Fields of Study

  • Mathematics

Readers

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  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.