Model Predictive Control for Dynamic Unreliable Resource Allocation

Abstract

In this paper, we consider a class of unreliable resource allocation problems where resources assigned may fail to complete a task, and the outcomes of past resource allocations are observed before new resource allocations are selected. The resulting temporal allocation problem is a stochastic control problem, with a state space and control space that grow exponentially in cardinality with the number of tasks. We introduce an approximation by enlarging the admissible control space, and show that this approximation can be solved exactly and efficiently. The approximation is used in a model predictive control (MPC) algorithm. For single resource problems, the MPC algorithm completes over 98 percent of the task value completed by an optimal dynamic programming algorithm in over 1,000 randomly generated problems. On average, it achieves 99.5 percent of the optimal performance while requiring over 6 orders of magnitude less comnutation.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2002
Accession Number
ADA409519

Entities

People

  • David A. Castañón
  • Jerry M. Wohletz

Tags

Communities of Interest

  • Air Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Aircrafts
  • Algorithms
  • Computations
  • Computer Programming
  • Control Systems
  • Dynamic Programming
  • Evolutionary Algorithms
  • Model Predictive Control
  • Optimization
  • Probability
  • Stochastic Control

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development
  • Strategic Security Studies

Technology Areas

  • Space
  • Space - Spacecraft Maneuvers