  We present a new neuroadaptive architecture: Deep Neural Network based Model
Reference Adaptive Control (DMRAC). Our architecture utilizes the power of deep
neural network representations for modeling significant nonlinearities while
marrying it with the boundedness guarantees that characterize MRAC based
controllers. We demonstrate through simulations and analysis that DMRAC can
subsume previously studied learning based MRAC methods, such as concurrent
learning and GP-MRAC. This makes DMRAC a highly powerful architecture for
high-performance control of nonlinear systems with long-term learning
properties.
