By Topic

Hidden Markov model-based learning controller

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jie Yang ; Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Yangsheng Xu ; C. S. Chen

Presents a method to learn control strategy by using a hidden Markov model (HMM), i.e., modeling a feedback controller in HMM structure. HMM is a powerful parametric model for non-stationary pattern recognition and is feasible for characterisation of a doubly stochastic process involving observable actions and a hidden decision making process. The control strategy is encoded by HMMs through a training process. The trained model is 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

Published in:

Intelligent Control, 1994., Proceedings of the 1994 IEEE International Symposium on

Date of Conference:

16-18 Aug 1994