By Topic

Neural Network Model Predictive Control with Genetic Algorithm Optimization and Its Application to Turbofan Engine Starting

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

2 Author(s)
Bo Yu ; Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China ; Jihong Zhu

Turbofan engine starting is one of the most important procedures during the whole process of job, but also very complicated due to its nonlinear dynamic working procedure. Recognizing the weaknesses of predict model and traditional algorithm for rolling optimization to deal with strong nonlinear systems, this paper presents neural network model predictive control method with genetic algorithm optimization, and uses this method to devise an optimal controller for turbofan engine starting. Experiment results show that under the premise of accurate limits, we can obtain the optimal fuel supply rate with enough precision.

Published in:

Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2010 2nd International Conference on  (Volume:2 )

Date of Conference:

26-28 Aug. 2010