title: 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.
authors: State Key Laboratory of Management and Control for Complex Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. dong.shen@ia.ac.cn
authors: Dong
authors: D
authors: Shen
authors: Zhongsheng
authors: Z
authors: Hou
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005246:Feedback; D008962:Models, Theoretical