The Robotics Institute

Carnegie Mellon Robotics Institute

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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 humans decision making process.

Notes

Grant ID: DACA76-89-C-0014, DAAE07-90-C-R059

Number of pages: 18

Text Reference

Jie Yang and Yangsheng Xu, "Hidden Markov Model for Control Strategy Learning," tech. report CMU-RI-TR-94-11, Robotics Institute, Carnegie Mellon University, May, 1994

BibTeX Reference

@techreport{Yang_1994_330,
   author = "Jie Yang and Yangsheng Xu",
   title = "Hidden Markov Model for Control Strategy Learning",
   booktitle = "",
   institution = "Robotics Institute",
   month = "May",
   year = "1994",
   number= "CMU-RI-TR-94-11",
   address= "Pittsburgh, PA",
}

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