Nonlinear system identification and trajectory tracking using dynamic neural networks

We analyze nonlinear identification and trajectory tracking using a dynamic neural network, with the same state space dimension as the system. We assume the system space state completely measurable. The identification error is formulated, and by means of a Lyapunov-like analysis we determine stability conditions for this error. Then we analyze the trajectory tracking error stability for the nonlinear system previously identified. The final structure of our scheme is composed by two parts: the neural network identifier and the tracking controller. As our main original contribution, we establish two theorems: the first one gives a bound for the identification error and the second one establish a bound for the tracking error.