Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
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.

The purchase and pricing options are temporarily unavailable. Please try again later.
1 Author(s)
Li Ganping ; Inf. Eng. Sch., Nanchang Univ., Nanchang, 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:

Control Conference (CCC), 2010 29th Chinese

Date of Conference:

29-31 July 2010