Using adaptive recurrent neural networks for chaos control

摘要

This paper proposes a new adaptive control scheme, based on a dynamic neural network, for trajectory tracking of unknown nonlinear plants. The main components of this scheme include a neural identifier and a control law, which together guarantee the desired trajectory tracking performance. Stability of the tracking error is analyzed by using the Lyapunov function method and the inverse optimal control approach. The scheme is tested by simulations on examples of complex dynamical systems: chaos stabilization and synchronization.