@misc{Wang_Rui_Q-learning,
 author={Wang, Rui and Zhuang, Zhihe and Tao, Hongfeng and Paszke, Wojciech (1975- ) and Stojanovic, Vladimir},
 howpublished={online},
 language={eng},
 abstract={This paper proposes a Q-learning based fault estimation (FE) and fault tolerant control (FTC) scheme under iterative learning control (ILC) framework. Due to its repetitive property of the demand on the control actuator, ILC is sensitive to actuator faults. Moreover, unknown faults could bring uncertainties to the system dynamics, which is a challenge to the control performance.},
 abstract={Therefore, this paper introduces Q-learning algorithm to estimate the unknown actuator faults without need of prior knowledge for controller recon_guration. Then, the design of FTC adopts the norm-optimal iterative learning control (NOILC) framework, where the controller is adjusted based on the FE results from Q-learning in real time to counteract the inuence of faults. Finally, the simulation of a mobile robot veri_es the e_ectiveness of the proposed algorithm.},
 type={artykuł},
 title={Q-learning based fault estimation and fault tolerant iterative learning control for MIMO systems},
 keywords={iterative learning control, fault estimation, fault tolerant control, Q-learning, MIMO systems},
}