  This letter proposes a convolutional neural network (CNN)-based adaptive
controller wtih three notable features: 1) it determines control input directly
from historical sensor data (in an end-to-end process); 2) it learns the
desired control policy during real-time implementation without using a
pretrained network (in an online adaptive manner); and 3) the asymptotic
tracking error convergence is proven during the learning process (to deliver a
stability guarantee). An adaptive law for learning the desired control policy
is derived using the gradient descent optimization method, and its stability is
analyzed based on the Lyapunov approach. A simulation study using a
control-affine nonlinear system demonstrated that the proposed controller
exhibits these features, and its performance can be tuned by manipulating the
design parameters. In addition, it is shown that the proposed controller has a
superior tracking performance to that of a deep neural network (DNN)-based
adaptive controller.
