@misc{Tao_Hongfeng_Robust,
 author={Tao, Hongfeng and Wei, Junyu and Hao, Shoulin and Paszke, Wojciech (1975- ) and Rogers, Eric},
 howpublished={online},
 language={eng},
 abstract={Iterative Learning Control (ILC) is renowned for its capability to achieve precise tracking control for systems with repetitive actions at a fixed time interval. However, pursuing the dual objective of highprecision tracking and rapid convergence is a persistent challenge in the field of learning control. To address this problem, a novel ILC method is designed for a class of discrete-time linear systems subject to non-repetitive disturbances in this paper.},
 abstract={Particularly, the updating term in ILC is constructed inspired by the principle of sliping mode control (SMC), which results in the learning process being divided into two distinct stages: a rapid reaching stage and a slow sliding stage. As a result, a balance between convergence speed and tracking performance can be ensured via the proposed ILC method. In addition, to attenuate the effects of non-repetitive disturbances, the disturbance compensation mechanism is integrated into the proposed ILC method.},
 abstract={Moreover, the optimal value of the learning gain can be determined using the predicted root mean square (RMS) errors of subsequent iterations, eliminating the need for additional tuning actions. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed new ILC method.},
 type={artykuł},
 title={Robust Indirect-Type Iterative Learning Control Design for Batch Processes with State Delay, Non-repetitive Uncertainties and Disturbances},
 keywords={batch processes, iterative learning control, time delay, timeand batch-varying uncertainties, generalized extended state observer},
}