Chaos synchronization via adaptive recurrent neural control

This paper proposes a new 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 trajectory tracking performance. Stability of the tracking control is analyzed by using the Lyapunov function method, and the structure is tested by simulations on an example of complex dynamical systems: chaos synchronization.