@misc{Zhuang_Zhihe_Data-enabled,
 author={Zhuang, Zhihe and González, Rodrigo A. and Tao, Hongfeng and Paszke, Wojciech (1975- ) and Oomen, Tom},
 howpublished={online},
 language={eng},
 abstract={Iterative learning control (ILC) is an intelligent control methodology for tackling iteration-invariant exogenous inputs. It is of great significance to develop its extrapolation for more general repetitive tasks with mutual similarity, e.g., tasks with different time scales. In practice, discrete-time ILC with sampling behavior for time-scale-varying tasks suffers from the failure of perfect corresponding learning and environment-dependent iteration-varying disturbances.},
 abstract={This paper develops a novel direct databased ILC algorithm using off-policy Q-learning for tasks with varying time scales, enabling the robust learning of an optimal ILC policy from experimental input/output (I/O) data. From a two-player zero-sum game perspective, the iteration-varying disturbance generated from the varying time scales of repetitive tasks is tackled quantitatively with a preset disturbance attenuation level.},
 abstract={Further, to emphasize the importance of theoretical guarantees of reinforcement learning (RL)-based ILC designs, the data efficiency of the developed algorithm is enhanced based on Willems` Fundamental Lemma, and a rigorous convergence analysis is given. The simulation model of an F-16 aircraft autopilot is employed to show the effectiveness of the developed approach.},
 type={artykuł},
 title={Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks},
 keywords={iterative learning control, time-scale-varying task, data-based control, reinforcement learning},
}