Iterative learning control with unknown control direction: a novel data-based approach
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.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):2237-2249Type
ArticleLanguage
enISSN
1045-92271941-0093
ae974a485f413a2113503eed53cd6c53
10.1109/TNN.2011.2175947