Design of neural stabilizing controller for nonlinear systems via Lyapunov's direct method

摘要

This paper is concerned with a neural stabilizing controller of general nonlinear systems. The stabilizing state feedback control law is approximated with a multi-layered neural network. Connection weights in the neural controller are determined by a mm-max algorithm such that the Lyapunov stability theorem holds via a control Lyapunov function.