Inverse optimal design for trajectory tracking with input saturations via adaptive recurrent neural control

This paper is related to trajectory tracking problem for nonlinear systems, with unknown parameters, unmodelled dynamics and input saturations. A high order recurrent neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then a control law, which stabilizes the tracking error dynamics, is developed using the inverse optimal control approach, recently introduced to nonlinear systems theory. Tracking error boundedness is established as a function of a design parameter. The applicability of the approach is illustrated via simulations, by synchronization of nonlinear oscillators.