By Topic

Altitude control of aircraft using coefficient-based policy method

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

5 Author(s)
Ju Jiang ; College of Automation, Nanjing University of Aeronautics and Astronautics, Jiangsu, China ; Huajun Gong ; Jianye Liu ; Haiyan Xu
more authors

This paper proposes a coefficient-based policy searching method, the direct policy search (DPS), for searching (learning) and construct policies for controlling the altitude of an aircraft. The DPS is a new and efficient reinforcement learning (RL) strategy combined with genetic algorithms (GAs). Specifically, an optimal policy in DPS consists of a set of coefficients which are learned using GA-based RL (GARL). The proposed method for learning optimal policy is demonstrated in controlling the complicated altitude system of a Boeing 747 aircraft whose solution space consists of 20 variables. Simulation results show that this new approach produces competitive performances with the traditional algorithms such as the classical state-feedback algorithm and the pure RL algorithm.

Published in:

Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on

Date of Conference:

4-7 May 2008