Abstract
This paper proposes a new data-driven method for addressing fault tolerant control problems. Unlike existing model-based or data-driven methods, the proposed method realizes fault tolerant control without knowing any system model parameter or performing any model identification. Q-learning severs as a key tool in this procedure. In addition, unlike conventional Q-learning algorithms used in the control community, the new proposed one can be applied in weakly stochastic environments, which facilitates the application of the fault tolerant control method in real industrial occasions. A DC motor simulation example demonstrates the effectiveness of the proposed method.
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