By Topic

Inverse model-based and feedback-assisted iterative learning control for a class of batch processes

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

1 Author(s)
Ganping Li ; Information Engineering School, Nanchang University, 330031, China

This paper presents a inverse model-based and feedback-assisted iterative learning control (ILC) for a class of batch processes. The dynamics of the processes can be represented by the first-order plus dead time (FOPDT) model. The ILC algorithm is derived based on the inverse model. The robustness of the proposed strategy for the batch processes in the presence of uncertainties in modeling is analyzed. Sufficient conditions guaranteeing convergence of tracking error are stated and proven. Simulation shows that the ILC strategy can improve the process performance gradually as a batch process repeated even there are model mismatches and disturbances.

Published in:

Proceedings of the 29th Chinese Control Conference

Date of Conference:

29-31 July 2010