A large mount of data is indispensable in deep learning. The learning results can be different because of the noise or contaminated tags. So in this paper, a controller design method is proposed to reduce the influence due to noise or damaged label. Our method is based on backstepping control method and observer. In our work, an adaptive function is designed to eliminate the influence of the unmodelable part of the system because of the contaminated tags. For the noise, the observer is used to accurately estimated and effectively compensated. Experimental results show the effectiveness of our method. Our modified system has good performance and can accurately response the input training data in the case of the unmodelable part of the system and the external noise.