Trajectory tracking via adaptive dynamic neural control

Presents an adaptive control structure, based on a dynamic neural network for trajectory tracking of unknown nonlinear plants. The main components of this structure include a neural identifier and a control law, which together guarantee the desired tracking. stability of the tracking error is analyzed by means of the Lyapunov function methods. Applicability of the proposed structure is tested by simulations on two complex examples.