Accession Number:

ADA282846

Title:

Hidden Markov Model for Control Strategy Learning

Descriptive Note:

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA ROBOTICS INST

Personal Author(s):

Report Date:

1994-05-01

Pagination or Media Count:

24.0

Abstract:

This report presents a method for learning a control strategy using the hidden Markov model HMM, i.e., developing a feedback controller based on HMMs. The HMM is a parametric model for non-stationary pattern recognition and is feasible to characterize a doubly stochastic process involving observable actions and a hidden decision pattern. The control strategy is encoded by HMMs through a training process. The trained models are then employed to control the system. The proposed method has been investigated by simulations of a linear system and an inverted pendulum system. The HMM-based controller provides a novel way to learn control strategy and to model the human decision making process

Subject Categories:

  • Operations Research
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE