Hidden Markov Model for Control Strategy Learning

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

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1994
Accession Number
ADA282846

Entities

People

  • Jie Yang
  • Yangsheng Xu

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Case Studies
  • Computational Science
  • Control Systems
  • Hidden Markov Models
  • Linear Systems
  • Markov Models
  • Multiple Input Multiple Output
  • Neural Networks
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Recognition
  • Signal Generators
  • Signal Processing
  • Simulations
  • Stochastic Processes

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.
  • Structural Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems