By Topic

Applying model predictive control in automotive

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

6 Author(s)
Hong Chen ; State Key Laboratory of Automotive Simulation and Control, Department of Control Science and Engineering (Campus NanLing), Jilin University, China ; Shuyou Yu ; Xiaohui Lu ; Fang Xu
more authors

Model predictive control (MPC), also called moving horizon control or receding horizon control, is one of the most successful and the most popular advanced control methods. The basis of MPC is the online solution of a constrained optimization problem updated by the actual state. The obtained control is injected into the system until the next sampling time, while the procedure is repeated whenever new measurements are available. Due to its ability to handle nonlinearity, to include various types of models predicting the future dynamics, to take time-domain constraints into account explicitly and to coordinate multiple performance requirements in the sense of optimization, MPC has become an attractive feedback strategy to design control systems in industrial applications over the last two decades.

Published in:

Intelligent Control and Automation (WCICA), 2012 10th World Congress on

Date of Conference:

6-8 July 2012