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Learning from demonstration (LfD) is a powerful technique for teaching robots new behaviors by mimicking human demonstrations. One widely used approach in LfD is Behavior Cloning (BC) with human-in-the-loop control. In this method, data collected from humans demonstrations is utilized to create a non-linear controller by learning a mapping from states to desired actions. In this study, we propose a novel BC system that its structure mirrors the structure of an error-based feedback controller. This design choice is based on the premise that embedding such a structure within the learning model can endow our system with a prior bias, resulting in an inherent advantage over controller-agnostic BC systems. This thesis details the components of this novel BC model and demonstrates its application on a two degrees-of-freedom robotic system across various tasks as a proof of concept. To assess the system's effectiveness, we conducted systematic experiments comparing it to a controller-agnostic BC system. The results reveal that our proposed model significantly outperforms the baseline, suggesting it is a promising candidate for LfD tasks where the demonstration data can be assumed to be generated by a feedback controller, especially in resource-scarce conditions.