Neural network-based adaptive robust control for a class of uncertain systems with measurement noise

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance-oriented control laws for a class of uncertain systems whose output is corrupted by external disturbances. The derived adaptive-robust control schemes not only guarantee all the signals are bounded in the closed loop, but also make the system preserve certain prescribed properties. The cone-bounded assumption on the uncertain dynamics is removed via neural networks. The feedback information is the state with measurement noise.