@misc{Tao_Hongfeng_Reinforcement,
 author={Tao, Hongfeng and Huang, Yuan and Liu, Tao and Paszke, Wojciech (1975- )},
 howpublished={online},
 language={eng},
 abstract={To tackle the time and batchwise uncertainty involved in nonlinear batch process, this paper proposes a deep reinforcement learning (DRL) based ILC scheme via Koopman operator. Using the Koopman operator, the original nonlinear system is reformulated into a high-dimensional linear space form. Then, a DRL agent with neural network is introduced into the 2D ILC framework to compensate for non-repetitive uncertainty.},
 abstract={Meanwhile, the convergence conditions of the proposed ILC scheme are analyzed with a proof through the linear matrix inequality. The proposed method is subsequently applied to a continuous stirring tank reactor (CSTR), and the output of the highdimensional linear model is found to be almost identical to that of the original nonlinear model, with an output error of less than 5%.},
 abstract={In contrast to the linearized ILC near the equilibrium point, the control performance of the ILC under the high-dimensional model based on Koopman operator is conspicuously augmented. Eventually, the utilisation of deep reinforcement learning to address non-repetitive uncertainty and model mismatch significantly enhances the control performance in comparison to dynamic iterative linearisation and PD-type classical ILC approach.},
 type={artykuł},
 title={Reinforcement learning based iterativa learning control for nonlinear batch process with non-repetitive uncertainty via Koopman operator},
 keywords={iterative learning control, nonlinear batch process, Koopman operator, deep reinforcement learning, non-repetitive uncertainty},
}