Output tracking with constrained inputs via inverse optimal adaptive recurrent neural control

This paper extends previous results to the output tracking problem of nonlinear systems with unmodelled dynamics and constrained inputs. A recurrent high order neural network is used to identify the unknown system dynamics and a learning law is obtained using the Lyapunov methodology. A stabilizing control law for the output tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law for nonlinear systems with constrained inputs.