Iterative learning control with unknown control direction: a novel data-based approach
| dc.contributor.author | Shen, Dong | en_GB |
| dc.contributor.author | Hou, Zhongsheng | en_GB |
| dc.date.accessioned | 2013-03-25T13:05:29Z | |
| dc.date.available | 2013-03-25T13:05:29Z | |
| dc.date.issued | 2011 | |
| dc.identifier.citation | Shen, D. and Hou, Z. (2011) 'Iterative Learning Control With Unknown Control Direction: A Novel Data-Based Approach' IEEE Transactions on Neural Networks 22 (12):2237-2249 | en_GB |
| dc.identifier.issn | 1045-9227 | |
| dc.identifier.issn | 1941-0093 | |
| dc.identifier.doi | 10.1109/TNN.2011.2175947 | |
| dc.identifier.uri | http://hdl.handle.net/10547/275872 | |
| dc.description.abstract | Iterative learning control (ILC) is considered for both deterministic and stochastic systems with unknown control direction. To deal with the unknown control direction, a novel switching mechanism, based only on available system tracking error data, is first proposed. Then two ILC algorithms combined with the novel switching mechanism are designed for both deterministic and stochastic systems. It is proved that the ILC algorithms would switch to the right control direction and stick to it after a finite number of cycles. Moreover, the input sequence converges to the desired one under the deterministic case. The input sequence converges to the optimal one with probability 1 under stochastic case and the resulting tracking error tends to its minimal value. | |
| dc.language.iso | en | en |
| dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_GB |
| dc.relation.url | http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6087286 | en_GB |
| dc.rights | Archived with thanks to IEEE Transactions on Neural Networks | en_GB |
| dc.subject | iterative learning control (ILC) | en_GB |
| dc.subject | iterative learning control | en_GB |
| dc.title | Iterative learning control with unknown control direction: a novel data-based approach | en |
| dc.type | Article | en |
| dc.contributor.department | Chinese Academy of Sciences | en_GB |
| dc.identifier.journal | IEEE Transactions on Neural Networks | en_GB |
| html.description.abstract | Iterative learning control (ILC) is considered for both deterministic and stochastic systems with unknown control direction. To deal with the unknown control direction, a novel switching mechanism, based only on available system tracking error data, is first proposed. Then two ILC algorithms combined with the novel switching mechanism are designed for both deterministic and stochastic systems. It is proved that the ILC algorithms would switch to the right control direction and stick to it after a finite number of cycles. Moreover, the input sequence converges to the desired one under the deterministic case. The input sequence converges to the optimal one with probability 1 under stochastic case and the resulting tracking error tends to its minimal value. |