Back to Search
Start Over
An Iterative Learning Control Algorithm With Gain Adaptation for Stochastic Systems.
- Source :
- IEEE Transactions on Automatic Control; Mar2020, Vol. 65 Issue 3, p1280-1287, 8p
- Publication Year :
- 2020
-
Abstract
- This paper proposes an iterative learning control (ILC) algorithm with gain adaptation for discrete-time stochastic systems. The algorithm is based on Kesten's accelerated stochastic approximation (SA) algorithm. The gain adaptation uses only tracking error information, and, hence, is a data-driven adaptation approach. If stochastic noises account for a small proportion of the tracking error, the learning gain matrix remains constant with a high probability. If stochastic noises dominate the tracking error, the learning gain matrix is decreasing. Therefore, the new ILC algorithm converges more quickly than existing SA-based algorithms. In addition, the classic P-type ILC law for noise-free systems is a special case of the new ILC algorithm. The behaviors of the proposed ILC algorithm are demonstrated through examples. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189286
- Volume :
- 65
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Automatic Control
- Publication Type :
- Periodical
- Accession number :
- 143314026
- Full Text :
- https://doi.org/10.1109/TAC.2019.2925495