Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
697627 | Automatica | 2007 | 6 Pages |
Abstract
The paper proposes a noise tolerant iterative learning control (ILC) for a class of linear continuous-time systems, which achieves high-precision tracking for uncertain plants by iteration of trials in the presence of heavy measurement noise. The robustness against measurement noise is achieved through (i) projection of continuous-time I/O signals onto a finite-dimensional parameter space, (ii) using error data of all past iterations via an integral operation in the learning law and (iii) noise reduction by H2H2 optimization subject to a specified convergence speed of the ILC.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Toshiharu Sugie, Fumitoshi Sakai,